Assessing and / or reducing binding affinity of ligands to cytochrome p450

Abstract

The present disclosure relates to methods of assessing and / or reducing binding affinity of a test ligand to a cytochrome P450 (CYP) protein. The method can include determining a binding affinity of the test ligand to the CYP protein; building one or more three-dimensional (3D) models for the test ligand bound to the CYP protein; and validating at least one of the one or more 3D models based on the binding affinity. Systems related to such methods are also disclosed herein.

Description

ASSESSING AND / OR REDUCING BINDING AFFINITY OF LIGANDS TOCYTOCHROME P450CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of priority to U.S. Application No. 63 / 657,425, filed on June 7, 2024, the contents of which are hereby incorporated by reference.FIELD

[0002] The present disclosure relates to methods of assessing and / or reducing binding affinity of a test ligand to a cytochrome P450 (CYP) protein, as well as systems and devices for performing such methods.BACKGROUND

[0003] Metabolism of a drug can affect its therapeutic effect, as well as its dosage regime. For example, a lead compound optimized for therapeutic activity in vitro can exhibit undesirable metabolism activity in vivo. In drug discovery, there is a need to reduce metabolism of a drug candidate by certain proteins, such as CYP. Current approaches for reducing oxidative metabolism by CYP include, e.g., chemical modification of a drug candidate at the site of metabolism. Modifications can include substitution of a metabolically labile group (e.g., a hydrogen atom) with another less labile group (e g., a halogen atom, such as fluorine, or deuterium). Yet, such an approach can be limited and may not be effective if, for instance, the modified drug candidate can be affected by alternate metabolic pathways to provide alternate metabolites, such that the previously identified metabolically labile group no longer provides metabolic stability to the alternate metabolites. Thus, predicting the effect of certain modifications on overall metabolism of a drug candidate can be challenging. Accordingly, there is a need for improved methods of reducing CYP metabolism of drug candidates.SUMMARY

[0004] The present disclosure relates to methods of assessing and / or reducing binding affinity of a test ligand to a cytochrome P450 (CYP) protein. In some embodiments, the test ligand is a compound (e.g., a lead compound) having a non-optimized clearance rate.

[0005] Accordingly, in one non-limiting aspect, the present disclosure relates to a method of assessing and / or reducing binding affinity of a first test ligand to a first cytochrome P450 (CYP)protein, the method including: determining a first binding affinity (Ksl) of the first test ligand to the first CYP protein; building one or more three-dimensional (3D) models for the first test ligand bound to the first CYP protein; and validating at least one of the one or more 3D models based on said Ksl, wherein the at least one 3D model provides a predicted binding affinity of the first test ligand by the first CYP protein.

[0006] In some embodiments, the method further includes (e.g., prior to said determining): obtaining absorbance spectra for the first test ligand at one or more ligand concentrations in the presence of the first CYP protein, wherein an absorbance spectrum is obtained at each ligand concentration. In some embodiments, the absorbance spectra provide said KS1and / or a first Hill coefficient (HHI); and / or the absorbance spectra include a Type I spectra, a reverse Type I spectra, or a Type II spectra.

[0007] In some embodiments, the method further includes (e.g., after said validating): predicting one or more modifications to the first test ligand to provide a modified first test ligand having lower binding to the first CYP protein, as compared to binding of the first test ligand to the first CYP protein. In some embodiments, one or more modifications to a ligand includes inclusion of one or more heteroatoms, replacement of hydrogen with halo, replacement of amino with carbonyl, and / or a positional isomer.

[0008] In some embodiments, the method further includes (e.g., after said predicting): providing the modified first test ligand; and determining a modified first binding affinity ( / fs™od) of the modified first test ligand to the first CYP protein. In some embodiments, Ksr?odis greater than Ksl. In some embodiments, the modified first test ligand includes a longer half-life (fi 2) than the first test ligand.

[0009] In some embodiments, the method further includes (e.g., prior to said determining KJi°d): obtaining absorbance spectra for the modified first test ligand at one or more ligand concentrations in the presence of the first CYP protein, wherein an absorbance spectrum is obtained at each ligand concentration.

[0010] In some embodiments, said building includes predicting a modified binding mode for the modified first test ligand to the first CYP protein. In some embodiments, the modified binding mode differs from a first binding mode for the first test ligand to the first CYP protein.

[0011] In some embodiments, said predicting the modified binding mode includes: comparing K^odto KS1to determine a difference between '"1lodand Kslreceiving a templateligand-biomolecule structure, the template ligand-biomolecule structure comprising a template ligand docked in the binding site of the biomolecule, wherein the template ligand comprises the first test ligand, and wherein the biomolecule comprises the first CYP protein; comparing a pharmacophore model of the template ligand to a pharmacophore model of a target ligand, wherein the target ligand comprises the modified first test ligand; overlapping the pharmacophore model of the target ligand with the pharmacophore model of the template ligand while the template ligand is in the binding site of the biomolecule; and predicting the docked position of the target ligand in the binding site of the biomolecule based on a position of the pharmacophore model of the target ligand when overlapped with the pharmacophore model of the template ligand, wherein the docked position comprises the modified binding mode.

[0012] In any embodiment herein, said building includes predicting a docked position of the first test ligand in a binding site of the first CYP protein. In some embodiments, said predicting the docked position includes: receiving a template ligand-biomolecule structure, the template ligand-biomolecule structure including a template ligand docked in the binding site of the biomolecule, wherein the biomolecule includes the first CYP protein; comparing a pharmacophore model of the template ligand to a pharmacophore model of a target ligand, wherein the target ligand includes the first test ligand; overlapping the pharmacophore model of the target ligand with the pharmacophore model of the template ligand while the template ligand is in the binding site of the biomolecule; and predicting the docked position of the target ligand in the binding site of the biomolecule based on a position of the pharmacophore model of the target ligand when overlapped with the pharmacophore model of the template ligand.

[0013] In any embodiment herein, said validating includes computing a free energy calculation for the first test ligand in a binding site of the first CYP protein. In some embodiments, said computing the free energy calculation includes free energy perturbation (FEP), molecular mechanics with generalized Born and surface area solvation (MM / GBSA), molecular mechanism with Poisson-Boltzmann and surface area solvation (MM / PBSA), thermodynamic integration, or metadynamics.

[0014] In any embodiment herein, KS1is determined based on absorbance within a range from about 300 to 405 nm, about 380 to 390 nm, about 390 to 420 nm, or about 420-440 nm; or wherein KS1is determined based on a difference in absorbance within a range from about 250 nm to 550 nm, as compared to a baseline absorbance over that same range.

[0015] In any embodiment herein, KS1is indicative of a transition from a hexacoordinate, low spin iron complex of the first CYP protein to a pentacoordinate, high spin complex of the first CYP protein.

[0016] In any embodiment herein, the first test ligand includes a congeneric ligand from a plurality of test ligands.

[0017] In any embodiment herein, the first CYP protein is selected from the group consisting of CYP1A1, CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP2J2, CYP3A4, CYP3A5, and CYP3A7, or a modified form thereof, and / or a fragment thereof.

[0018] In some embodiments, the method further includes: determining a second binding affinity (Ks2) of the first test ligand to a second CYP protein that is different than the first CYP protein; building one or more three-dimensional (3D) models for the first test ligand bound to the second CYP protein; validating at least one of the one or more 3D models based on said Ks2, wherein the at least one 3D model provides a predicted binding affinity of the first test ligand by the second CYP protein; and optionally predicting one or more modifications to the first test ligand to provide a modified first test ligand having lower binding to the first CYP protein and to the second CYP protein, as compared to binding of the first test ligand to the respective first or second CYP protein.

[0019] In any embodiment herein, the method further includes: determining a first Hill coefficient ( ZHI) of the first test ligand to the first CYP protein.

[0020] In another non-limiting aspect, the present disclosure relates to a method of assessing and / or reducing binding affinity of an m number of test ligands to an n number of CYP proteins, the method including: obtaining absorbance spectra for each zzzthtest ligand at ap number of ligand concentrations in the presence of each zzthCYP protein, wherein the absorbance spectra include an m x n x p number of absorbance spectra obtained for each zzzthtest ligand at each / ?thconcentration with each A11CYP protein; determining an m x n number of binding affinitiestest ligand to each zzthCYP protein based on the m x n x p number of absorbance spectra; building one or more three-dimensional (3D) models for each zzzthtest ligand bound to each zzthCYP protein; validating at least one of the one or more 3D models based on saidsm n, wherein the at least one 3D model provides a predicted binding affinity of at least one zzzlhtest ligand by at least one zzthCYP protein; and predicting one or more modifications to atleast one mthtest ligand to provide a modified test ligand having lower binding (e.g., characterized by a modified binding affinity ofto one or more A11CYP proteins, as compared to binding of the at least one mihtest ligand to corresponding wthCYP protein (e.g., wherein is greater than Ksm n). In some embodiments, each of m, n, and p is, independently, an integer of one or more.

[0021] In another non-limiting aspect, the present disclosure relates to a computer system including: at least one processor; a preparation module, stored in memory and coupled to at least one processor, wherein the preparation module is programmed to receive information including a first binding affinity (Xsi) of a first test ligand to a first CYP protein; a build module, stored in memory and coupled to at least one processor, wherein the build module is programmed to build one or more three-dimensional (3D) models for the first test ligand bound to the first CYP protein; and a validation module, stored in memory and coupled to at least one processor, wherein the validation module is programmed to validate at least one of the one or more 3D models based on said Ksi.

[0022] In another non-limiting aspect, the present disclosure relates to a non-transitory computer readable storage medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to assess and / or reduce binding affinity of a first test ligand to a first CYP protein, each prediction including causing the computer to perform the steps of: receiving information including a first binding affinity (Ksl) of a first test ligand to a first cytochrome P450 (CYP) protein; one or more three- dimensional (3D) models for the first test ligand bound to the first CYP protein; and validate at least one of the one or more 3D models based on said Ksl. Additional details follow.Definitions

[0023] As used herein, the term “about” means + / - 10% of any recited value. As used herein, this term modifies any recited value, range of values, or endpoints of one or more ranges.

[0024] By “protein,” “peptide,” or “polypeptide,” as used interchangeably, is meant any chain of more than two amino acids, regardless of post-translational modification (e.g., glycosylation or phosphorylation), constituting all or part of a naturally occurring polypeptide or peptide, or constituting a non-naturally occurring polypeptide or peptide, which can includecoded amino acids, non-coded amino acids, and / or modified amino acids (e.g., chemically and / or biologically modified amino acids).

[0025] The term “conservative amino acid substitution” refers to the interchangeability in proteins of amino acid residues having similar side chains (e.g., of similar size, charge, and / or polarity). For example, a group of amino acids having aliphatic side chains consists of glycine (Gly, G), alanine (Ala, A), valine (Vai, V), leucine (Leu, L), and isoleucine (He, I); a group of amino acids including proline (Pro, P), glycine (Gly, G), and alanine (Ala, A); a group of amino acids having aliphatic-hydroxyl side chains consists of serine (Ser, S) and threonine (Thr, T); a group of amino acids having amide containing side chains consisting of asparagine (Asn, N) and glutamine (Gin, Q); a group of amino acids having aromatic side chains consists of phenylalanine (Phe, F), tyrosine (Tyr, Y), and tryptophan (Trp, W); a group of amino acids having basic side chains consists of lysine (Lys, K), arginine (Arg, R), and histidine (His, H); a group of amino acids having acidic side chains consists of glutamic acid (Glu, E) and aspartic acid (Asp, D); and a group of amino acids having sulfur containing side chains consists of cysteine (Cys, C) and methionine (Met, M). Exemplary conservative amino acid substitution groups are valine-leucine-isoleucine, phenylalaninetyrosine, lysine-arginine, alanine-valine, glycine-serine, glutamate-aspartate, and asparagineglutamine.

[0026] The term “fragment” is meant a portion of a nucleic acid or a polypeptide that is at least one nucleotide or one amino acid shorter than the reference sequence. This portion contains, preferably, at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of the entire length of the reference nucleic acid molecule or polypeptide. A fragment may contain 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1250, 1500, 1750, 1800 or more nucleotides; or 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 640 amino acids or more. In another example, any polypeptide fragment can include a stretch of at least about 5 (e.g., about 10, about 20, about 30, about 40, about 50, or about 100) amino acids that are at least about 40% (e g., about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 87%, about 98%, about 99%, or about 100%) identical to any of the sequences described herein can be utilized in accordance with the disclosure. In certain embodiments, a polypeptide to be utilized in accordance with the disclosure includes 2, 3, 4, 5, 6, 7, 8, 9, 10, or more mutations (e.g., one or more conservative amino acidsubstitutions, as described herein). Tn yet another example, any nucleic acid fragment can include a stretch of at least about 5 (e.g., about 7, about 8, about 10, about 12, about 14, about 18, about 20, about 24, about 28, about 30, or more) nucleotides that are at least about 40% (about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 87%, about 98%, about 99%, or about 100%) identical to any of the sequences described herein can be utilized in accordance with the disclosure.

[0027] Other features and advantages of the present disclosure will be apparent from the following detailed description, the figures, and the claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The following drawings illustrate certain embodiments of the features and advantages of this disclosure. These embodiments are not intended to limit the scope of the appended claims in any manner. Like reference symbols in the drawings indicate like elements.

[0029] FIG. 1A-1D shows non-limiting binding between a potential ligand and a potential cytochrome P450 (CYP) protein. Provided are (A) a non-limiting catalytic cycle for binding of ligand RH to an iron site of a CYP protein, (B) a graph showing non-limiting changes in relative absorbance upon ligand binding to the CYP protein, (C) a graph showing a non-limiting difference spectrum by subtracting a spectrum of the unbound CYP protein from the bound CYP protein with the ligand, and (D) a graph showing a non-limiting titration plot of a ligand to derive binding affinity Ks.

[0030] FIG. 2A-2B shows non-limiting methods of assessing and / or reducing binding affinity of a test ligand.

[0031] FIG. 3 shows another non-limiting method of assessing and / or reducing binding affinity of a test ligand.

[0032] FIG. 4 shows yet another non-limiting method of assessing and / or reducing binding affinity of a test ligand.

[0033] FIG. 5A-5B shows (A) a non-limiting correlation plot for FEP predicted AG (labeled as “AGFEP+”) and experimental AG (labeled as “AGexp”) and (B) a non-limiting statistics chart for a series of ligands bound to a model for CYP3A4.

[0034] FIG. 6A-6B shows (A) a non-limiting image of a ligand bound to a non-limiting 3D model of CYP2D6 and (B) a non-limiting chart showing the effects of modifying the initial ligand to provide reduced binding to CYP2D6.

[0035] FIG. 7 shows non-limiting correlation plots for FEP predicted AG (labeled as “AGFEP+”) and experimental AG (labeled as “AGexp”) and non-limiting statistics charts for a series of ligands bound to a model for CYP3A4 (top) or CYP2D6 (bottom).

[0036] FIG. 8 shows a summary comparison of azithromycin and erythromycin.

[0037] FIG. 9 shows the chemical structures of azithromycin and erythromycin, in which atoms common to both molecules are highlighted in gray and differences are not highlighted. A carbonyl (dashed oval) in erythromycin is converted to a tertiary amine (dashed oval) in azithromycin.

[0038] FIG. 10 shows the metabolism site of erythromycin by CYP.DETAILED DESCRIPTION

[0039] The present disclosure relates to methods of assessing and / or reducing binding affinity of ligands to one or more cytochrome P450 (CYP) proteins. In vivo metabolism of ligands can occur via one or more CYP proteins, and the methods herein can be used to assess the effect of such CYP protein interactions on a compound including such ligands. In some embodiments, the methods herein include the use of three-dimensional (3D) models, which can employ induced-fit methods to predict binding of a test ligand to the CYP protein. Such models can be validated with the use of free energy perturbation (FEP) methods (or others) and experimentally determined values (e.g., from binding affinity Ks, potency as determined by a half maximal inhibitory concentration (IC50), etc ).

[0040] Described herein are methods of assessing and / or reducing binding affinity of one or more test ligands to one or more CYP proteins. In some embodiments, the method includes determining at least one binding affinity (Ks) of at least one test ligand to at least one CYP protein; building one or more 3D models for the test ligand bound to the CYP protein; and validating at least one of the 3D models based on said Ks. Any number of test ligands and CYP proteins may be assessed. For example, models can be built and validated for each mthtest ligand interacting with each nihCYP protein. In some embodiments, a 3D model can be built for each mihtest ligand with each nthCYP protein, in which each of m and n is, independently, an integer of 1 or more. In turn, such models can be used to provide predicted / fsm nvalues for binding of each mihtest ligand to each nihCYP protein; and validation can include comparing predicted values with respective experimentally determinedvalues.

[0041] In some embodiments, a series of ligands include an m number of test ligands. In some embodiments, a congeneric series of ligands include an m number of test ligands, in which a first test ligand (m = 1) is a ligand from a first test compound and each m-1 number of ligands (e.g., each / if1' ligand, in which m > 1) is a congeneric ligand of the first test ligand. In turn, each compound associated with each zwthligand, in which m >1, can be a congener of the first compound. Each test compound can include a combination of a plurality of ligands, and one or more of such ligands (for each test compound) may be assessed by the methods herein. In some embodiments, a plurality of test compounds can be used in the methods herein. In turn, each test compound (of the plurality) can include a combination of a plurality of ligands, and one or more of such ligands (for each test compound) may be assessed by the methods herein.

[0042] Models can be further implemented with modified ligands (e.g., in a congeneric series of ligands) to assess the effect of such modifications on binding affinity to the CYP protein. In this way, modifications can be selected to reduce a rate of metabolism by the CYP protein or by one or more CYP proteins. For example and without limitation, methods herein can be used to provide a predicted ;™odvalue for binding of a modified first test ligand to a CYP protein; and validation can include comparing a predicted '"1lodvalue with an experimentally determined KS1value. In some non-limiting embodiments, such models can be extended to a series including (i) a first test ligand (in which m = 1) and (ii) an m- \ number of ligands (e.g., each / wthligand, in which m > 1) that are congeneric ligands of the first test ligand. In turn, methods herein can be used to provide predictedvalues for binding of each modifiedtest ligand to a CYP protein; and validation can include comparing a predicted K<!^odvalue with a respective experimentally determined Ksmvalue.

[0043] In some embodiments, a series of CYP proteins include an n number of CYP proteins. In some embodiments, each CYP protein in the series can be different isoforms. Non-limiting examples of CYP proteins are provided herein. In some embodiments, the series of CYP proteins includes at least CYP3A4 and / or CYP3A5. In some embodiments, the series of CYP proteins includes one or more of CYP1A1, CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP2J2, CYP3A4, CYP3A5, and CYP3A7. In certain embodiments, the series of CYP proteins includes one or more members of the CYP 19 family, such as CYP19A1.

[0044] Models can be further implemented with other CYP proteins. As described herein, various isoforms of CYP proteins exist, and modeling can be performed to capture binding by different CYP proteins. For example and without limitation, methods herein can be used to provide predicted KS1 nvalues for binding of a first test ligand to an A11CYP protein; and validation can include comparing predicted / fsl nvalues with respective experimentally determined KS1 nvalues. In some embodiments, such models can be to a series of ligands include an m number of test ligands with another series of proteins include an n number of CYP proteins. Then, methods herein can be used to provide predicted Ksni)nvalues for binding of each / / z"1test ligand to each / / lhCYP protein; and validation can include comparing a predicted ^sm,n value with a respective experimentally determined / fsm,nvalue. In some embodiments, the series of ligands can include a congeneric series of ligands, and methods herein can be used to provide predicted Ks™°n values for binding of each / wthmodified test ligand to each nthCYP protein; and validation can include comparing a predictedvalue with a respective experimentally determinedsm nvalue.

[0045] Metabolism of one or more test ligands by one or more CYP proteins can be understood as a catalytic cycle, in which binding of the ligand to the CYP protein can be spectroscopically analyzed. For example, FIG. 1A provides a non-limiting schematic of a catalytic cycle 100 of a CYP protein. Initially, a ligand RH binds the hexacoordinate ferric complex 101 to provide a pentacoordinate ferric complex 102. During this binding step, the heme complex shifts from a low spin state to a high spin state, in which this shift can be detected spectroscopically as a change in absorbance at particular wavelengths. For instance, type I spectra. Alternatively, the ligand may itself coordinate with the heme preserving a hexacoordinate complex but leading to a unique type II, or through other binding mechanisms lead to a reverse type I spectra. These spectra can be distinguished by their spectral peaks having differing maximum absorbances (e.g., as seen in insert 150 in FIG. 1A). For example and without limitation, type I spectra typically have a maximum absorbance (Xmax) of about 358-390 nm and a minimum absorbance (Xmin) of about 416 to 424 nm; type II spectra typically have a max of about 425 to 440 nm and a Xmin of about 390 to 415 nm; and reverse type I spectra typically have a Xmaxof about 418 to 424 nm and a Xmin of about 390 to 394 nm.

[0046] Then, various electron reduction and oxygen transfer processes provide a pentacoordinate ferrous complex 103, a hexacoordinate ferric superoxido adduct 104, and aferric peroxide complex 105. Subsequent protonation provides a ferryl oxido intermediate 106, which may then be regenerated into the hexacoordinate ferric complex 101. Various other pathways (e.g., oxidase shunt, peroxide shunt, or autoxidation shunt pathways) may exist to transfer protons and / or oxygen species.

[0047] Without wishing to be limited by mechanism or theory, the CYP protein facilitates the conversion or metabolism of diverse types of functional groups in a ligand. Furthermore, the catalytic cycle of CYP proteins can be understood as having alternate reaction pathways that facilitate metabolism. Thus, modification of a ligand at a single site of metabolism may not be effective, as other sites may then be metabolized. Accordingly, described herein are methods that reduce binding of a ligand to the CYP protein. As the catalytic cycle for metabolism by CYP proteins begins with ligand binding, reduced binding should provide a reduced rate of metabolism. In some embodiments, this approach may be effective even when one or more subsequent steps within the catalytic cycle is predominantly irreversible (e.g., a subsequent step of converting the ferric peroxide complex 105 to the ferryl oxido intermediate 106, which occurs after the binding step of the hexacoordinate ferric complex 101 and the ligand RH to form the pentacoordinate ferric complex 102).

[0048] Upon modifying an initial ligand (e.g., an initial test ligand), the modified ligand (e.g., modified test ligand) may or may not bind to the CYP protein in a similar manner as the initial ligand. For example, and without limitation, the predicted binding affinity of the modified ligand may be so reduced that another binding mode may be energetically favorable, as compared to the initial binding mode between the initial ligand and the CYP protein. In this nonlimiting example, the predicted binding affinity of the modified ligand may differ from an experimentally determined binding affinity value (e.g., as determined by using spectroscopic data, as described herein). A significant difference between predicted and experimentally determined binding affinity values can suggest the presence of a differing 3D model that may more accurately capture this difference in binding affinity values. The different 3D model may constitute a dominant binding mode between the modified ligand and the CYP protein that differs from an initial binding mode between the initial ligand and the CYP protein.Accordingly, in some embodiments, methods herein can include predicting a modified binding mode for the modified ligand (e.g., as described herein).

[0049] Furthermore, as the binding step (of the ligand to the hexacoordinate ferric complex 101) can be spectroscopically determined, the methods herein can be validated by use of one or more experimentally determined binding affinity values (Ks, e.g.,sm nfor each mthtest ligand with each nthCYP protein or / fSm°n foreachw / thmodified test ligand with each «thCYP protein). As seen in FIG. IB, a type I interaction is shown, in which an initial curve 164 provides relative absorbance of a non-limiting CYP protein with unbound ligand. As can be seen, the initial curve 164 is characterized by a maximum absorbance Ab (e g., at about 420 nm). Upon exposing the CYP protein to increased concentrations of a ligand (or with increased exposure time), the spectrum transitions to a final curve 162 that is characterized by a maximum absorbance Aa(e.g., at about 390 nm). Thus, the extent of binding between a ligand and the CYP protein can be determined by the change in absorbance, e.g., an increase 166 in absorbance at a first maximum absorbance Aa(e.g., at about 390 nm in FIG. IB) and a decrease 168 in absorbance at a second maximum absorbance Ab (e.g., at about 420 nm in FIG. IB). As seen in FIG. 1C, provided is a difference spectrum 170, which was obtained by subtracting a spectrum of the unbound CYP protein (e.g., initial curve 164) from the bound CYP protein with a certain concentration of the ligand (e.g., final curve 162). The difference spectrum 170 is characterized by a maximum relative absorbance in proximity to or at a first maximum absorbance Aaand by a minimum relative absorbance in proximity to at the second maximum absorbance Ab. These spectra can be obtained for each ligand and for various concentrations of each ligand, and the difference in absorbance between Aaand Ab (AAa-b) can be used to determine the proportional amount of bound and unbound species. As seen in FIG. ID, a titration plot 180 using AAa-b values can be used to derive binding affinity Ks(e.g., a concentration of ligand at which 50% binding is observed). Depending on the type of spectra (e.g., type 1, type II, or reverse type I), differing values for the first and second maximum absorbance can be employed.

[0050] Any useful measurement can be used to determine the extent of binding between the test ligand and the CYP protein. In some embodiments, the measurement is a binding affinity Ks, e.g., Ksm nfor each / »l1' test ligand with each nthCYP protein or Ks°n for each mthmodified test ligand with each / / thCYP protein). Binding affinity (e.g., Ksm n, KSm°n, or others described herein) can be determined in any useful manner. In some embodiments, binding affinity is based on absorbance within a range from about 300 to 405 nm, about 380 to 390 nm, about 390 to 420 nm, or about 420 to 440 nm. In some embodiments, binding affinity is determined based on adifference in absorbance within a range from about 390 nm to 420 nm, as compared to a baseline absorbance within a range from about 380 to 390 nm. In some embodiments, binding affinity is indicative of a transition from a hexacoordinate, low spin iron complex of the CYP protein to a pentacoordinate, high spin complex of the CYP protein. Binding affinity can have any useful value (e.g., from about 0.001 pM to about 500 pM). Determination of the binding affinity values can include, e.g., performing equilibrium titrations, kinetic analysis, and / or data analysis (e.g., linear and / or non-linear regression, hyperbolic fits, etc.).

[0051] In some embodiments, the measurement is a Hill coefficient (ZZHI). A Hill coefficient (e.g., ZHI, «Hm,n, or others herein) can be determined for eachtest ligand with each z?thCYP protein). For example and without limitation, determination of the Hill coefficient can include performing kinetic analysis and / or data analysis (e.g., linear and / or non-linear regression, hyperbolic fits, etc.).

[0052] One or more measurements (e.g., any herein) can be provided by analyzing absorbance spectra, which can be determined in any useful manner. In some embodiments, one or more spectra are obtained by ultraviolet-visible (UV-Vis) spectroscopy, fluorescence spectroscopy, circular dichroism (CD) spectroscopy, surface plasmon resonance (SPR) spectroscopy, and the like. In some embodiments, absorbance spectra are obtained by UV-Vis spectroscopy to determining binding states of the CYP protein to the test ligand (e.g., by determining one or more values for binding affinity). Other assays may be employed to determine a binding affinity. For example and without limitation, such assays can include competitive binding assays, inhibitor screening kits, etc. in any useful format (e.g., UV-Vis, fluorescence, CD, SPR, etc., in which additional components can include one or more probes, substrates, metabolites, and the like).

[0053] In some embodiments, a plurality of spectra is obtained. For example, an absorbance spectrum can be obtained for each mthtest ligand at one or more ligand concentrations. In some embodiments, a p number of ligand concentrations is employed, in which p is an integer of 1 or more. Each ligand concentration can span over any useful range to provide a useful binding curve. For example and without limitation, the ligand concentration can be selected from a range from about 0 pM to about 200 pM. Within this range, any useful p number of ligand concentrations can be selected. In some embodiments, the method includes obtainingabsorbance spectra for the test ligand at one or more ligand concentrations in the presence of the CYP protein, in which an absorbance spectrum is obtained at each ligand concentration.Three-dimensional (3D) models

[0054] A 3D model can be used to understand the effect of a test ligand with a particular CYP protein. In some embodiments, building such as 3D model can begin with an experimentally determined structure of the CYP protein. Non-limiting examples of CYP proteins and their associated PDB entry numbers are provided herein. In some embodiments, building a 3D model may employ a plurality of experimentally determined structures of the CYP protein. The structure of the CYP protein can be unbound or bound (e.g., to a ligand, an inhibitor, or the like).

[0055] A 3D model may be further adapted with induced-fit docking (IFD) or other methodologies. For example and without limitation, IFD may be employed to place an / A11test ligand in the experimentally determined structure of an nlhCYP protein. In some embodiments, binding between the A111test ligand and the nthCYP protein can be accommodated by changing a conformation of the / / lhCYP protein to avoid clashes. In some embodiments, binding between the ot111test ligand and the wthCYP protein can be accommodated by changing a conformation of the nthCYP protein to provide a heme of the / 7thCYP protein to be in proximity to a metabolism site of the m&test ligand. In some embodiments, the metabolism site can include a metabolically labile group of the ?Mthtest ligand (e g., in which the metabolically labile group can be hydrogen, amino, a weaker electron-donating group, or a stronger electron-donation group). In some embodiments, binding between thelhtest ligand and the n&CYP protein can be occur through a lock-key mode, in which the conformation of the 77thCYP protein may not need to under significant conformational changes. Modified models may be further validated (e.g., using methods including one or more free energy calculations, as described herein).

[0056] In some embodiments, building a 3D model can include a docked position of an mthtest ligand in a binding site of an / / thCYP protein. A docked position can be predicted in any useful manner. In some embodiments, predicting can include: receiving a template ligandbiomolecule structure; comparing a pharmacophore model of the template ligand to a pharmacophore model of a target ligand (e.g., an m^ test ligand); overlapping the pharmacophore model of the target ligand with the pharmacophore model of the template ligand while the template ligand is in the binding site of the biomolecule; and predicting the docked position ofthe target ligand in the binding site of the biomolecule based on a position of the pharmacophore model of the target ligand when overlapped with the pharmacophore model of the template ligand. In some embodiments, the template ligand-biomolecule structure includes a template ligand docked in the binding site of the / / lhCYP protein. In some embodiments, the template ligand is the first test ligand (e.g., an / 77thtest ligand, in which m = 1).

[0057] In some embodiments, building a 3D model can include a modified binding mode of an 777thtest ligand in a binding site of an 7?thCYP protein. In some embodiments, the 777thtest ligand is a modified test ligand (e.g., an 777thtest ligand, in which m = 1 ) A modified binding mode can be a modified docked position (e.g., a docked position that is modified, as compared to an initial docked position for the initial test ligand with an 77thCYP protein). A modified binding mode be predicted in any useful manner. In some embodiments, predicting can include: receiving a template ligand-biomolecule structure, wherein the template ligand includes the first test ligand (e g., an 777thtest ligand, in which 777 = 1); comparing a pharmacophore model of the template ligand (e.g., an 777thtest ligand, in which 777 = 1) to a pharmacophore model of a target ligand (e.g., an 77?thtest ligand, in which m 1); overlapping the pharmacophore model of the target ligand with the pharmacophore model of the template ligand while the template ligand is in the binding site of the biomolecule; and predicting the docked position of the target ligand in the binding site of the biomolecule based on a position of the pharmacophore model of the target ligand when overlapped with the pharmacophore model of the template ligand. In some embodiments, the template ligand-biomolecule structure includes a template ligand docked in the binding site of the 77thCYP protein. In some embodiments, the template ligand is the first test ligand (e.g., an 77?thtest ligand, in which 777 = 1). In some embodiments, the target ligand is the first test ligand (e.g., an 777thtest ligand, in which 777 1).

[0058] Further methods for building and using 3D models are described in Int. Pub. Nos. WO 2019 / 079585 and WO 2008 / 141260, each of which is incorporated herein by reference in its entirety.Validation

[0059] Any useful technique may be employed to validate a model. Such techniques can include the use of free energy calculations to compute a ligand’s affinity or interaction with a CYP protein. Non-limiting examples of such techniques can include free energy perturbation (FEP), molecular mechanics with generalized Born and surface area solvation (MM / GBSA),molecular mechanism with Poisson-Boltzmann and surface area solvation (MM / PBSA), thermodynamic integrations (TI), umbrella sampling, metadynamics, lambda dynamics, alchemical metadynamics, nonequilibrium work, etc.

[0060] In some embodiments, validation can include one or more calculations of binding affinity between the test ligand and the CYP protein, the relative binding free energy of the test ligand and a modified test ligand with the CYP protein, the absolute binding free energy of the test ligand with the CYP protein, and the like. Such calculations can be determined for any useful combination of test ligand (e.g., an mthtest ligand) and CYP protein (e.g., / / thCYP protein). In some embodiments, a set of congeneric ligands with associated binding affinities is employed for model validation.

[0061] A validated model can reproduce any useful, experimentally measurable value. In some embodiments, the validated model can reproduce a binding affinity (e.g., / fsmothers described herein for each mthtest ligand and each HthCYP protein).

[0062] In some embodiments, the validated model reproduces a series of binding affinities Ksm,n, inwhich m > 1, and each mthtest ligand comprises a congeneric ligand from a plurality of test ligands (e.g., a plurality of m test ligands). By employing a series of ligands, relative FEP can be obtained and used to predict one or more changes in binding affinity between any pair of mihtest ligands (e.g., between the first test ligand and the second test ligand, between an m-lthtest ligand and an mihtest ligand, etc.). In some embodiments, relative changes in binding affinity can facilitate prediction, as compared to prediction of a single ligand’s absolute binding affinity, because relative binding affinity can take advantage of cancellation of error.

[0063] Further methods for validating 3D models are described in Int. Pub. Nos. WO 2019 / 040444, WO 2015 / 099637, and WO 2014 / 151310 and in U.S. Pat. Pub. No. 2023 / 0260601, each of which is incorporated herein by reference in its entirety.Modification

[0064] Based on models described herein (e.g., a 3D model or a validated form thereof), an initial test ligand may be modified, and a further model (e.g., a further 3D model or a validated form thereof) may be generated with the modified ligand. Such a model may assess the effect of that modification on the model and, thus, may be employed to determine one or more properties associated with that modification. Such properties can include a modified binding affinity foreach zzzthtest ligand with each z?thCYP protein (e.g., a binding affinity indicated as Ks™°n). In turn, the modified binding affinity can indicate an improved or beneficial property, such as an increased value of the binding affinity (e.g.,greater than KS1 n, a binding affinity for the initial test ligand with an zzthCYP protein), which is indicative of weaker binding to the CYP protein. By decreased binding of a ligand to the CYP protein, a rate of metabolism for that ligand can be reduced. In the case of CYP, once it has been determined how the drug binds, changes can be introduced to reduce binding affinity. If binding between a ligand to the CYP protein under Michaelis-Menten kinetics is reduced, there is an increase in the Michaelis-Menten constant (Km). Increasing the Km, decreases the initial rate of metabolism (v;) of the ligand by the CYP protein, in which where Vmaxis the limiting rate at saturating substrateconcentration and [S] is the concentration of the substrate (here, the ligand). As can be seen, vtis inversely proportional to Km, such that decreasing vtcan be achieved by increasing Km. While Kmcharacterizes metabolism of the ligand to the CYP protein, the dissociation constant (or binding affinity, Ks) characterizes the initial binding interaction of the ligand to the CYP protein, and Km> Ks. Taken together, weaker binding provides an increased Ksand an increased Km, which in turn provides a decreased rate of metabolismAs the rate of metabolism decreases, the ligand (or drug) remains longer in a subject’s body. In this way, modifying binding affinity Kscan provide a reduced rate of metabolism.

[0065] A ligand can be modified in any useful manner. Non-limiting examples of modification includes inclusion of one or more heteroatoms (e.g., nitrogen, oxygen, phosphorous, sulfur, or halo (e.g., fluoro, chloro, bromo, or iodo)), replacement of hydrogen with halo, replacement of amino with carbonyl, and / or a positional isomer. Yet other non-limiting modifications can include replacement of a group with a stronger electron-donating group or a weaker electron-donating group; or replacement of a group with a stronger electron-withdrawing group or a weaker electron-withdrawing group.

[0066] A modified binding affinity can be determined in any useful way (e.g., as described herein for Wsm,n). For example, binding affinity values (Ks. e.g., Asm nfor each mthtest ligand with each nthCYP protein) can be determined of any m number of test ligands with any n number of CYP proteins. Similarly, modified binding affinity values (e.g.,for each z?zthmodified test ligand with each zzthCYP protein) can be determined of any m number of modifiedtest ligands with any n number of CYP proteins. In some embodiments, binding affinity can be determined for a series of modified ligands include an m number of modified test ligands. In some embodiments, the series is a congeneric series, in which a first test ligand (m = 1) can be a ligand from a first test compound and each m-1 number of ligands (e.g., each mthligand, in which m > 1) can be a congeneric ligand of the first test ligand. In determining binding affinity, any p number of ligand concentrations can be employed. In some embodiments, determining a modified binding affinity includes obtaining absorbance spectra for the modified / «lhtest ligand at one or more ligand concentrations in the presence of an / / thCYP protein. In some embodiments, an absorbance spectrum is obtained at each ligand concentration for the p number of ligand concentration (e.g., in which p can be any described herein).

[0067] A modified binding affinity can have any useful absolute value or relative value (e.g., as compared to the initial binding affinity). Modified binding affinity can have any useful value (e g., from about 0.001 pM to about 500 pM). An improvement in binding affinity can be determined by comparing a modified binding affinity to an initial binding affinity (e.g., a first binding affinity Ksl) to be improved. In some embodiments, the improvement can be an increase of 2 fold, 5 fold, 10 fold, 100 fold, or even 1000 fold (in which larger values in improvement indicate a reduced rate of metabolism) (e.g., an increase in 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 600, 700, 800, 900, 1000, or more times). In some embodiments, an improvement in binding affinity can be determined by its effect on the half-life (ti 2) of the test ligand (or the compound including the test ligand). In some embodiments, the modified mlhtest ligand has a longer O 2 than the first test ligand (e.g., an increase in 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more times).

[0068] Upon obtaining one or more modified ligands with desired predicted binding affinity values, such modified ligand(s) can be synthesized and tested to obtain experimentally determined binding affinity values. Such experimentally determined values may be compared to relevant predicted binding affinity values, which may be used to further validate, refine, or provide 3D models. If the rate of metabolism or the binding affinity has been sufficiently reduced, then further iterations may not be required. In some embodiments, the rate of metabolism or the binding affinity can be further established for a different CYP protein. If the rate of metabolism or the binding affinity has not been sufficient reduced, then further modifications to the test ligand can be made to reduce its binding affinity. Alternatively, furthermodifications can be made to a different ligand on the same compound. Such modifications can be combined, in which differing ligands of a single compound can be identified (e.g., a first test ligand and a second test ligand) and then differing congeneric series can be formed for each ligand (e.g., a first congeneric series for the first ligand having an mi number of test ligands, as well as a second congeneric series for the second ligand having an m2 number of test ligands, in which each of mi and m2 can be any integer described herein for m). Combinations of each type of ligand can be provided to determine its effect on binding affinity and / or rate of metabolism (e g., an effect for a compound including both an mithtest ligand and an m2thtest ligand). Then, further iterations of modeling, validation, and / or optional additional modification can be performed until obtained a desired binding affinity and / or a desired rate of metabolism.Ligands

[0069] Any useful test ligand can be employed (e.g., as a first test ligand, a second test ligand, an m&test ligand, etc.). In some embodiments, the mthtest ligand (e.g., a first test ligand, a second test ligand, etc.) comprises a congeneric ligand from a plurality of test ligands (e.g., a plurality of m test ligands). Optionally, each wzthtest ligand can be associated with a respective binding affinity that can be experimentally determined (e.g., Asm for each / 7?11' test ligand).

[0070] In some embodiments, an initial ligand (e.g., an initial test ligand) may be modified to provide a desired property, e.g., on-target potency, off-target potency, or absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. In some embodiments, one or more modifications can include inclusion of one or more heteroatoms, replacement of hydrogen with halo, replacement of amino with carbonyl, and / or a positional isomer.

[0071] In some embodiments, a test ligand can include an entire structure of a test compound. In some embodiments, a test ligand can be a structural portion of a test compound. In some embodiments, a plurality of differing ligands can be assessed for a single test compound. In some embodiments, a plurality of differing ligands can be assessed for each of a plurality of test compounds. A test compound can include any useful class of compounds (e.g., substrates, inhibitors, and / or suicide substrates).

[0072] In some embodiments, a test compound can be described by a pharmacophore model, which in turn can include a combination of one or more pharmacophore features. In some embodiments, at least one of the pharmacophore features constitutes a test ligand. In some embodiments, the pharmacophore model constitutes a test ligand. Non-limiting pharmacophorefeatures can include, e.g., one or more hydrophobic types, aromatic types, hydrogen bond acceptor types, hydrogen bond donor types, cationic types, anionic types, and combinations of any of these. In some embodiments, a modified test ligand can include one or more modifications to one or more pharmacophore features.Cytochrome P450 (CYP) proteins

[0073] Any useful CYP protein can be employed (e.g., as a first CYP protein, a second CYP protein, an rC CYP protein, etc ), including human and / or non-human CYP proteins. In some embodiments, the CYP protein is selected from the group consisting of cytochrome P450 1A1 (CYP1A1), cytochrome P450 1A2 (CYP1A2), cytochrome P450 1B1 (CYP1B1), cytochrome P450 2A6 (CYP2A6), cytochrome P450 2A13 (CYP2A13), cytochrome P450 2B6 (CYP2B6), cytochrome P450 2C8 (CYP2C8), cytochrome P450 2C29 (CYP2C9), cytochrome P450 2C18 (CYP2C18), cytochrome P450 2C19 (CYP2C19), cytochrome P450 2D6 (CYP2D6), cytochrome P450 2E1 (CYP2E1), cytochrome P450 2J2 (CYP2J2), cytochrome P450 3A4 (CYP3A4), cytochrome P450 3A5 (CYP3A5), cytochrome P450 3A7 (CYP3A7), and cytochrome P450 aromatase (CYP 19) or a modified form thereof, and / or a fragment thereof. In some embodiments, the CYP protein can include one or more conservative amino acid substitutions (e.g., for any sequence provided herein by way of a UniProt Entry No.).

[0074] In some embodiments, the CYP protein is one or more of the following: human CYP1A1, UniProt Entry No. P04798 (PDB Entry Nos. 4i8v, 6dwn, 6dwm, 6o5y, 6udl, and 6udm); human CYP1A2, UniProt Entry No. P05177 (PDB Entry No. 2hi4); human CYP1B1, UniProt Entry No. QI 6678 (PDB Entry Nos. 3pm0 and 6iq5); human CYP2A6, UniProt Entry No. Pl 1509 (PDF Entry Nos. IzlO, Izl 1, 2fdu, 2fdv, 2fdw, 2fdy, 3ebs, 3t3q, 3t3r, 4ejj, and 4rui); human CYP2A13, UniProt Entry No. Q16696 (PDB Entry Nos. 2p85, 2pg5, 2pg6, 2pg7, 3t3s, 4ejg, 4ejh, and 4eji); human CYP2B6, UniProt Entry No. P20813 (PDB Entry Nos. 3ibd, 3qoa, 3qu8, 3ua5, 4i91 , 4rql, 4rrt, 4zv8, 5uap, 5uda, 5uec, 5ufg, and 5wbg); human CYP2C8, UniProt Entry No. P10632 (PDB Entry Nos. Ipq2, 2nnh, 2nni, 2nnj, and 2vn0); human CYP2C9, UniProt Entry No. Pl 1712 (PDB Entry Nos. Iog2, log5, lr9o, 4nz2, 5a5i, 5a5j, 5k7k, 5w0c, 5x23, 5x24, 5xxi, 6vlt, and 7rl2); human CYP2C18, UniProt Entry No. P33260 (PDB Entry Nos. 2cik and 2h6p); human CYP2C19, UniProt Entry No. P33261 (PDB Entry No. 4gqs); human CYP2D6, UniProt Entry No. P10635 (PDB Entry Nos. 2f9q, 3qm4, 3tbg, 3tda, 4wnt, 4wnu,4wnv, 4wnw, 4xry, 4xrz, 5tft, 5tfu, 6csb, and 6csd); human CYP2E1, UniProt Entry No. P05181 (PDB Entry Nos. 3e4e, 3e6i, 3gph, 3koh, 31c4, and 3t3z); human CYP2J2, UniProt Entry No. P51589; human CYP3A4, UniProt Entry No. P08684 (PDB Entry Nos. Itqn, IwOe, IwOf, IwOg, 2j0d, 2v0m, 3nxu, 3tjs, 3ual, 4d6z, 4d75, 4d78, 4d7d, 4i3q, 4i4g, 4i4h, 4k9t, 4k9u, 4k9v, 4k9w, 4k9x, 4ny4, 5alp, 5alr, 5g5j, 5te8, 5vc0, 5vcc, 5vcd, 5vce, 5vcg, 6bcz, 6bd5, 6bd6, 6bd7, 6bd8, 6bdh, 6bdi, 6bdk, 6bdm, 6da2, 6da3, 6da5, 6da8, 6daa, 6dab, 6dac, 6dag, 6daj, 6dal, 6ma6, 6ma7, 6ma8, 6oo9, 6ooa, 6oob, 6une, 6ung, 6unh, 6uni, 6unj, 6unk, 6unl, 6unm, 7ks8, 7ksa, 7kvh, 7kvi, 7kvj, 7kvk, 7kvm, 7kvn, 7kvo, 7kvp, 7kvq, 7kvs, 71x1, 7uay, 7uaz, 7uf9, 7ufa, 7ufb, 7ufc, 7ufd, 7ufe, 7uff, 8dyc, 8ewd, 8ewe, 8ewl, 8ewm, 8ewn, 8ewp, 8ewq, 8ewr, 8ews, 8exb, 8sol, 8so2, and 8spd); human CYP3A5, UniProt Entry No. P20815 (PDB Entry Nos. 5veu, 6mjm, 71ad, 7sv2, and 8sg5); human CYP3A7, UniProt Entry No. P24462 (PDB Entry Nos. 7mk8 and 8gk3); and human CYP7A1, UniProt Entry No. P22680 (PDB Entry Nos. 3dax, 3sn5, and 3v8d).

[0075] In some embodiments, the CYP protein (e.g., a first CYP protein, a second CYP protein, an u11' CYP protein, or any described herein) is selected from the group consisting of CYP1A1, CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP2J2, CYP3A4, CYP3A5, and CYP3A7, or a modified form thereof, and / or a fragment thereof.

[0076] In some embodiments, the CYP protein is from the CYP1 family (e.g., CYP1A1, CYP1A2, or CYP1B1), the CYP2 family (e g., CYP2A6, CYP2A13, CYP2B6, CYP2C8, CYP2C9, CYP2C18, CYP2C19, CYP2D6, CYP2E1, or CYP2J2), or the CYP3 family (e.g, CYP3A4, CYP3A5, or CYP3A7).

[0077] In some embodiments, the modified form of a CYP protein is a mutant or a variant of any described herein. In some embodiments, the CYP protein is a variant of CYP3A4 (e.g, human CYP3A4, UniProt Entry No. P08684). Non-limiting examples of variants of human CYP3A4 include one or more of the following positional mutations in a sequence optimally aligned with that provided for UniProt Entry No. P08684: A2D, A2G, A2T, A2V, L3P, L3V, I4T, P5S, D6E, A8P, E10Q, T1 II, W12C, L15P (allele CYP3A4*14), V17I, S18R, L19Q, V20A, V20L, V20M, L21V, L22V, T27I, H28Q, H28R, S29T, H30D, H30R, G31E, K34E, K35R, L36F, L36R, L36V, I38V, P39L, G40E, P41L, T42A, T42I, T42K, P43L, P43R, P43S, P45L, G48E, N49D, N49T, I50T, Y53H, H54R, K55Q, G56D (allele CYP3A4*7), F60L, M62K,E63*, E63K, C64S, K66R, K67N, K67R, K67T, Y68C, Y68H, G69R, W72G, W72L, G77R, G77V, Q78H, Q79R, V81A, V81L, L82V, M89V, I90T, I90V, K91Q, K96E, E97D, C98R, C98Y, Y99H, S1OOF, V101F, R105L, R105Q, R105W, R106K, R106T, P107L, P107R, G109D, G109R, G109S, G112R, F113I, F113L, F113S, M114K, S116C, SI 16T, Al 17T, Al 17V, Il 18F, Il 18V (allele CYP3A4*4), S119T, I120T, A121G, E122D, D123E, D123Y, E124G, K127N, L129I, R13O*, R130G, R130P, R130Q (allele CYP3A4*8), L132W, S134C, P135L, P135S, T136A, T136I, S139G, S139T, M145V, P147L, I148V, A15OS, Q151R, V155A, L160P, R162Q (allele CYP3A4*15), R162W, T166I, G167A, G167D, G167R, K168N, P169A, P169S, P169T, V170I (allele CYP3A4*9), K173E, K173R, D174E, D174H (allele CYP3A4*10), D174N, V175D, V175I, F176L, A178S, A178V, Y179S, S180N, M181T, I184T, T185A, T185S (allele CYP3A4*16), S186G, S186N, S186R, S188L, F189L, F189S (allele CYP3A4*17), V191A, V191E, N192K, N192S, I193M, I193V, D194N, D194Y, N198D, Q200*, Q200E, Q200H, D201N, P202L, P202R, P202S, E205D, E205G, E205K, N206Y, T207I, T207N, K208E, L210P, F213Y, D214G, D214N, L216F, D217N, P218R (allele CYP3A4*5), F219L, F220C, F220V, S222P (allele CYP3A4*2), S222T, I223L, I223M, I223V, F226L, P227L, P227T, F228L, L229F, L229R, I230V, P231L, I232V, L233P, E234G, V235A, L236*, I238T, C239F, P242L, R243T, N247D, N247I, L249* , K251T, S252A, S252T, V253A, R255K, R255T, E258V, S259G, R260C, R260H, E262K, D263N, H267N, R268*, R268Q, L272P, Q273R, D277N, S278P, K282E, E285G, S286P, K288R, A289S, A289V, S291C, S291F, D292E, D292N, L293P (allele CYP3A4*18), V296L, V296M, S299*, S299P, S299T, I3OOV, I301L, I301T, A3O5S, T309A, T309I, T310K, T310M, S3 UN, V313G, L314F, L314P, S315 A, S315F, Y319C, T323P, H324Q, Q328R, Q332R, E333K, E333Q, E334K, I335T, D336E, D336H, A337E, A337V, K342N, P345L, P345S, Y347C, T349N, Q352*, M353I, M353L, E354K, Y355C, Y355H, D357E, M358I, M358V, V359E, T363K, T363M, L364F, I369V, A370S, A370V, M371R, R372G, R372I, R372T, L373F (allele CYP3A4*12), E374D, R375K, R375M, K379R, E382D, M386I, I388F, P389L, P389S, V392M, M395I, M395V, I396T, P397L, S398N, S398R, Y399C, L401F, L401P, R403C, R403H, R403P, P405T, Y407*, W408*, W408R, P411A, P41 IL, E412K, P416L (CYP3A4*13), P416R, R418T, F419L, S420G, S420I, K421R, K424R, D425E, D425N, N426K, N426S, I427V, D428E, D428H, D428N, P429L, P429R, P429S, 143 IT, Y432F, T433A, T433I, P434A, G436R, S437T, G438V, P439S, N441D, G444A, M445I, M445T, M445V, R446K, L449F, M450T, M450V, M452L, M452T, M452V, K453N, L454F, L454I,L454P, I457V, R458I, V459L, L460F, N462K, F463C, S464F, S464T, P467A, P467S (allele CYP3A4H9), C468W, C468Y, E470K, Q472*, Q472H, Q472R, I473F, I473M, I473N, L477*, S478C, S478G, S478R, L479*, G480E, G481R, Q484R, P485R, K487E, P488H, V489I, V490F, L491P, L491Q, L491V, V493A, S495T, V500A, V500I, S501G, S501N, and G502E, in which indicates stop gained, “=” indicates silent, and “del” indicates deleted when referring to positional mutations.

[0078] In some embodiments, the CYP protein is a variant of CYP3 A5 (e.g., human CYP3A5, UniProt Entry No. P20815). Non-limiting examples of variants of human CYP3A5 include one or more of the following positional mutations in a sequence optimally aligned with that provided for UniProt Entry No. P20815: L3I, P5Q, L7S, A8V, V9E, V9L, El OK, W12*, L14I , L15R, Y23*, Y25H, T27N, R28C (allele CYP3A5*8), R28H, T29I, H30Y, F33L, F33S, K34R , L36R, I38V, P39S , T42I, P43H, P43L, L46F, L46M, V50I, L51F, S52F, Y53C, Y53H, R54C, R54G, R54H, R54S, Q55*, G56C, G56D, W58*, W58S, K59T, F60C , D61E, E63K, C64F, C64S, K67R, Y68*, K70N, W72*, W72*, G73*, G73R, T74K, T74M, G77A, P80R, L82M , L82R, L82V, T85R, D88H, D88N , V89M, R91K, V93L, L94Q, E97*, S100C, S100Y, T103I, T103K, N104I, R105*, R105G, R105Q, R106S, S107=, S107P, G112V, Ml 14V, S116T, L120S, D123E, D123V, E124D, E125K , W126R, K127R, R128K, I129T, R130Q, L132V, L133P, T136A, T136N, T138S, G140R, K141T, K143R, E144G, E144K, M145T, F146L, I148M, I148N, I148T, I149T, I149V, A150D, A150S, Q151L, Q151R, Y152*, Y152H, G153E , D154N, V157M , R158I, N159H , L160W, R161G, R162Q, R162W, A164T, A164V, E165G, E165Q, K166*, K166N, K168R, P169S, V170I, K173R, D174G, I175S, Y179*, Y179F, SI 801, D182N, V183G, T185I, G186A , T187I, S188*, S188T, F189L, V191M, I193L, I193V , D194H, D194N, S195C, S195F, S195P, N197K, Q200R (allele CYP3A5*4), D201H, D201N, P202L, P202S, F203C, F203S, E205D, S206R, F210S, K212N, F213V, G214V, P218A , L219S, F220C, I223V, L225F, P227S, P227T, F228C, F228L, F228L, F228S, L229F, L229P, T230I, P231T, F233L, F233S, N237T, V238A, V238D, L240P, K243E, D244Y , T245A, T245I, I246K, I246V, N247H , F248C, F248L, L249*, S250N, K251Q, R255G, M256I , M256K, K257M, S259G, R260H, R260P, N262D , N262S, D263N, Q265*, H267Q, R268*, R268L, R268Q, Q273R, L274P, M275I, M275R, I276S, I276T, D277E, D277G, D277Y, Q279R, N280H, S281*, S281L, K282N, G285*, E285K, S286F, H287L, A289P, A289V, L290=, E294K, L295F, L295I, A296T, A297V, S299A, S299T, I303L, F304S, Y307H, T309I, T309N, T310I, S3 HR, S311T, L314I,S315C, T317I, T317S, Y319H, A322D, P325S, D326N, V327I, K330E, L331P, Q332L, Q332P, E334K , I335T, D336N , D336Y, A337T (allele CYP3A5*9), L339F, P340S, N341S, A343S, A343V, P344L , P344Q, P345A, P345L, P345T, T346A, Y347C, V350A, V35OM, Q352P, Y355*, Y355C, D357N, M358L, M358V, V360M, N361S, E362*, E362G, T363I, L364H, L366*, A370P, I371T, I371V, L373I, R375G, C377R, K378E, D380G, V381I, N384H, G385R, V386I, F387I, F387Y, I388T, P389S, S392P, S392T, M393I, M393V, V394L, V395L, T398N (allele CYP3A5*2), L401P, H403R, D404Y, K406E, T409S, P411R, P411S, E412K, R415C, R415H, R415L, R415P, P416S, R418M, F419L, S420I, S420R, K423E, K423R, D424N, S425N, I426M, D427H, D427G, P428A, Y429S, I430T, I430V, Y431N, T432I, T432K, G435E, G435R, T436I, T436N, T436S, P438H , R439K, I442T, G443D, G443S, M444V, F446S, A447V, M449K, M449T, L453I, L453R, L453V, A454V, L455P, L455V, V458A, Q460*, N461H, S463missing, P466H, C467R, I472T, P473A, P473L, P473S, L476S, T478M, P484Q, P484S, K486E, P487S, I488T, V489I, V492M, D493N, R495T, G497V, T498N, L499P, L499V, S500G, G501*, and G501E, in which indicates stop gained, “=” indicates silent, “missing” indicates missing, and “del” indicates deleted when referring to positional mutations.

[0079] In some embodiments, the CYP protein is a variant of CYP2D6 (e.g., human CYP2D6, UniProt Entry No. P10635). Non-limiting examples of variants of human CYP2D6 include one or more of the following positional mutations in a sequence optimally aligned with that provided for UniProt Entry No. P10635: A5V, allele CYP2D6*87; VI IM, allele CYP2D6*35; R26H, alleles CYP2D6*21 and CYP2D6*46; R28C, allele CYP2D6*22; P34S, alleles CYP2D6*10 and CYP2D6*14; G42R, allele CYP2D6*12; A85V, allele CYP2D6*23; V104A, allele CYP2D6*88; T107I, allele CYP2D6*17; L142S, allele CYP2D6*89; K147R, allele CYP2D6*90; E155K, alleles CYP2D6*45A, CYP2D6*45B, and CYP2D6*4; C161S, allele CYP2D6*91; G169R, allele CYP2D6*14; G212E, alleles CYP2D6*6B and CYP2D6*6C; E215K; A237S, allele CYP2D6*33; T249P, allele CYP2D6*93; K281del, allele CYP2D6*9; R296C, alleles CYP2D6*2, CYP2D6*12, CYP2D6*14, CYP2D6*17, CYP2D6*45A, CYP2D6*45B, and CYP2D6*46; I297L, allele CYP2D6*24; H324P, allele CYP2D6*7; D337G, allele CYP2D6*94; R343G, allele CYP2D6*25; I369T, allele CYP2D6*26; E410K, allele CYP2D6*27; R440C; F457L, allele CYP2D6*97; H463D, allele CYP2D6*98; and S486T, alleles CYP2D6*2, CYP2D6*10, CYP2D6*12, CYP2D6*14, CYP2D6*17, CYP2D6*45A, CYP2D6*45B, and CYP2D6*46.

[0080] Yet other non-limiting examples of variants of human CYP include one or more of the following positional mutations: I462V, CYP1A1*2C, in a sequence optimally aligned with that provided for UniProt Entry No. P04798; R48G, A119S, and / or L432V, CYP IB 1*6, in a sequence optimally aligned with that provided for UniProt Entry No. Q16678; R128Q, CYP2A6*6, in a sequence optimally aligned with that provided for UniProt Entry No. Pl 1509; 147 IT, CYP2A6*7, in a sequence optimally aligned with that provided for UniProt Entry No. Pl 1509; R485L, CYP2A6*8, in a sequence optimally aligned with that provided for UniProt Entry No. Pl 1509; 147 IT and / or R485L, CYP2A6*10, in a sequence optimally aligned with that provided for UniProt Entry No. Pl 1509; S224P, CYP2A6*11, in a sequence optimally aligned with that provided for Uni rot Entry No. Pl 1509; V365M, CYP2A6*17, in a sequence optimally aligned with that provided for UniProt Entry No. Pl 1509; I328T, allele CYP2B6*, in a sequence optimally aligned with that provided for UniProt Entry No. P20813; K262R, CYP2B6*4, in a sequence optimally aligned with that provided for UniProt Entry No. P20813; R487C, CYP2B6*5, in a sequence optimally aligned with that provided for UniProt Entry No. P20813; Q172H and / or K262R, CYP2B6*6, in a sequence optimally aligned with that provided for UniProt Entry No. P20813; I269F, allele CYP2C8*2, in a sequence optimally aligned with that provided for UniProt Entry No. P10632; R139K and / or K399R, CYP2C8*3, in a sequence optimally aligned with that provided for UniProt Entry No. P10632; I264M, allele CYP2C8*4, in a sequence optimally aligned with that provided for UniProt Entry No. P10632; R144C, allele CYP2C9*2, in a sequence optimally aligned with that provided for UniProt Entry No. Pl 1712; I359L, allele CYP2C9*3, in a sequence optimally aligned with that provided for UniProt Entry No. Pl 1712; D360E, allele CYP2C9*5, in a sequence optimally aligned with that provided for UniProt Entry No. Pl 1712; 133 IV, allele CYP2C19*1B, in a sequence optimally aligned with that provided for UniProt Entry No. P33261; and W212*, allele CYP2C19*3, in a sequence optimally aligned with that provided for UniProt Entry No. P33261.Further methods

[0081] Methods herein can include any useful combination of operations to determine binding affinity of a ligand, build 3D model(s), validate 3D model(s), and predict modification to ligands to provided desired binding affinity. Methods herein can include determining any useful number of binding affinity values for any useful number and combination of test ligands, modified test ligands, and CYP proteins.

[0082] FIG. 2A provides a non-limiting method 200A for assessing and / or reducing binding affinity of a first test ligand to a first CYP protein. The method 200A can include an operation 201 of obtaining one or more absorbance spectra for a first test ligand at a plurality of ligand concentrations in the presence of a first CYP protein (e.g., any described herein). The first test ligand can be any ligand of a compound (e.g., a first test compound). Then, the method 200A can include an operation 202 of determining a first binding affinity (Asl) based on the absorbance spectra of the first test ligand. Any useful methodology can be used to determine KS1(e.g., as described herein). In some embodiments, KS1can be determined by spectroscopically measuring absorbance of the first CYP protein in the presence of differing concentrations of the first ligand.

[0083] The method 200A can include an operation 203 of building one or more 3D models for the first test ligand bound to the first CYP protein. In some embodiments, an initial 3D model can be employed to build further 3D models. For example and without limitation, if the first CYP protein is a CYP3A5, then an initial model can include any PDB model described herein (e.g., PDB Entry Nos. 5veu, 6mjm, 71ad, 7sv2, and 8sg5 for human CYP3A5, UniProt Entry No. P20815). In some embodiments, the initial model can be bound to an initial ligand that is comparable to the first test ligand.

[0084] The 3D model can then be validated based on experimentally determined values. For example, the method 200A can include an operation 204 of validating the 3D model(s) based on KS1(e.g., determined in operation 202). In some embodiments, validation can rely on stored information relating to binding affinity values that have been previously obtained.

[0085] Based on a validated 3D model, one or more modifications can be made to a ligand, and binding affinity of such modified ligands to the first CYP protein can be predicted. In some embodiments, the method 200A can include an operation 205 of predicting one or more modifications to the first test ligand to provide a modified first test ligand having lower binding to the first CYP protein, as compared to binding of the first test ligand to the first CYP protein. For example, the binding affinity of the modified first test ligand ( / <snJod) can be predicted by using the validated 3D model (e.g., as obtained from operation 204). In some embodiments, a binding affinity can be predicted for each proposed modification to the first test ligand. For example, an m number of modifications can be made to the first test ligand to provide an m number of modified first test ligands, such that an m number of binding affinity values ( / fsI]?1od)can be determined for each mthmodified first test ligand. Then, each / f™°dcan be compared to KS1(for the first test ligand) to determine whether the modification provided a decrease in relative binding (e g., / f™°d> Ksl) or an increase in relative binding (e.g., K’snI[1od< Ksi)

[0086] The method can include further optional operations. For example and without limitation, the method 200A can include an operation 206 of synthesizing the one or more modified first test ligands. For example, if an m number of modification are proposed and an m number of binding affinity values ( / fsod) are predicted, then the method can include synthesizing each ni'hmodified first test ligand. Such an approach can be used to further validate the model with such modified ligands. In another example, only a subset of the m number of modified ligands can be synthesized (e.g., each mthmodified first test ligand that is predicted to have a beneficial Ks™°dthat is indicative of weaker binding to the CYP protein.

[0087] Another optional operation 207 can include obtaining absorbance spectra for the one or more modified first test ligands. Such spectra can be obtained at a plurality of ligand concentrations in the presence of the first CYP protein and then analyzed to experimentally determined each Ksnmodfor any of the mihmodified first test ligands.

[0088] Operations may be performed in any useful sequence. For example, certain operations may be performed in parallel to other operations. In another example, indicated operations may be performed in a different order. For example, experimentally determined values of binding affinity may be performed at any time before validating a 3D model, such that the experimentally determined values are stored and then accessed for use during validation. FIG. 2B provides a non-limiting method 200B, which can include an operation 201 of obtaining one or more absorbance spectra for a first test ligand at a plurality of ligand concentrations in the presence of a first CYP protein (e.g., any described herein) and an operation 202 of determining a first binding affinity (Ksl) based on the absorbance spectra of the first test ligand. KS1can be determined at any time prior to validation of a 3D model and can be stored in any useful manner. The method 200B can then include an operation 203 of building one or more 3D models for the first test ligand bound to the first CYP protein, an operation 204 of validating the 3D model(s) based on KS1(e.g., determined in operation 202 in method 200B), and an operation 205 of predicting one or more modifications to the first test ligand to provide a modified first test ligand having lower binding to the first CYP protein, as compared to binding of the first test ligand to the first CYP protein. The method 200B can optionally include an operation 206 of synthesizingone or more of the modified first test ligands and / or an operation 207 of obtaining absorbance spectra for one or more of the modified first test ligands.

[0089] Methods herein can be performed for a plurality of test ligands. In some embodiments, each test ligand is a part of a congeneric series. In some embodiments, each test ligand is a part of a non-congeneric series. FIG. 3 provides a non-limiting method 300, which can include an operation 301 of obtaining one or more absorbance spectra for a plurality of test ligands (e.g., an m number of test ligands) in the presence of a first CYP protein (e.g., any described herein). Spectra can be obtained for a plurality of ligand concentrations for each test ligand (e.g., a p number of concentrations for each mlhtest ligand, in which each of p and m is, independently, an integer greater than 1).

[0090] The method 300 can include an operation 302 of determining a first binding affinity (Ksl) based on the absorbance spectra for each of the test ligand. In some embodiments, each binding affinity value can be provided as Ksmfor each mthtest ligand.

[0091] The method 300 can then include an operation 303 of building one or more 3D models for each test ligand bound to the first CYP protein. For example, one or more 3D models can be built for each mthtest ligand. The method 300 can include an operation 304 of validating the 3D model(s) based on KS1(e.g., determined in operation 302 in method 300, which may be provided as Ksmfor each mlhtest ligand) and an operation 305 of predicting one or more modifications to each test ligand to provide a modified test ligand having lower binding to the first CYP protein, as compared to binding of a corresponding test ligand to the first CYP protein. Any useful comparisons may be employed. For example, an mthmodified test ligand can be compared to any other z??11' modified test ligand, or an zz?11' modified test ligand can be compared to the first test ligand (e.g., prior to modification).

[0092] The method 300 can optionally include an operation 306 of synthesizing one or more of the modified test ligands and / or an operation 307 of obtaining absorbance spectra for one or more of the modified test ligands.

[0093] Methods herein can include determining binding affinity for a first CYP protein and a second CYP protein. In some embodiments, the method includes: determining a first binding affinity (Asi) of the first test ligand to a first CYP protein; and determining a second binding affinity Ka) of the first test ligand to a second CYP protein that is different than the first CYP protein. In some embodiments, the method can include: building one or more three-dimensional(3D) models for the first test ligand bound to the second CYP protein; validating at least one of the one or more 3D models based on said KS2, wherein the at least one 3D model provides a predicted binding affinity of the first test ligand by the second CYP protein; and optionally predicting one or more modifications to the first test ligand to provide a modified first test ligand having lower binding to the first CYP protein and to the second CYP protein, as compared to binding of the first test ligand to the respective first or second CYP protein.

[0094] Methods herein can include assessing and / or reducing binding affinity of an m number of test ligands to an n number of CYP proteins. In some embodiments, the method includes: obtaining absorbance spectra for each 777thtest ligand at a p number of ligand concentrations in the presence of each 77thCYP protein, wherein the absorbance spectra comprise an m x n x p number of absorbance spectra obtained for each mlhtest ligand at each pihconcentration with each 77thCYP protein; determining an m x n number of binding affinities (Xsm,n) of each «7l11test ligand to each 77thCYP protein based on the m x 77 x p number of absorbance spectra; building one or more three-dimensional (3D) models for each 777thtest ligand bound to each 77thCYP protein; validating at least one of the one or more 3D models based on said ?sm,n, wherein the at least one 3D model provides a predicted binding affinity of at least one 777thtest ligand by at least one 77thCYP protein; and predicting one or more modifications to at least one 777thtest ligand to provide a modified test ligand having lower binding to one or more 77thCYP proteins, as compared to binding of the at least one 777thtest ligand to corresponding 77thCYP protein. In some embodiments, each of 777, 77, and p is, independently, an integer of one or more.

[0095] FIG. 4 provides a non-limiting method 400, which can include an operation 401 of obtaining one or more absorbance spectra for an 777 number of test ligands in the presence of at least one of an 77 number of CYP proteins (e.g., any described herein). Spectra can be obtained for a p number of ligand concentrations for each 777thtest ligand in the presence of each 77thCYP protein. Each of m, n, and p can be, independently, any integer described herein. In some embodiments, each of 777, 77, and p is, independently, an integer of one or more. In some embodiments, each of m, n, and p can be same or different. In some embodiments, 777 is at least 1 (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, or 10) to at most 100 (e.g., at most 90, 80, 70, 60, 50, 40, 30, 20, or 10); 77 is at least 1 (e.g., at least 2, 3, 4, 5, or 6) to at most 10 (e.g., at most 9, 8, 7, 6, 5, 4, or 3); and p is at least 1 (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, or 10) to at most 50 (e.g., at most 40, 30, 20, 10, 8, or 6). The method 400 can include an operation 402 of determining a bindingaffinity based on the absorbance spectra for each ?Mthtest ligand with each nthCYP protein.

[0096] The method 400 can then include an operation 403 of building one or more 3D models for each mthtest ligand bound to each ? / 111CYP protein. For example, one or more 3D models can be built for each mthtest ligand bound to each «thCYP protein. The method 400 can include an operation 404 of validating the 3D model(s) based on Ksm n(e.g., determined in operation 402 in method 400) and an operation 405 of predicting one or more modifications to each «?11' test ligand to provide a respective modified test ligand having lower binding to one or more i?11’ CYP proteins, as compared to binding of a corresponding mlhtest ligand to the corresponding / / thCYP protein. The method 400 can optionally include an operation 406 of synthesizing one or more of the modified test ligands and / or an operation 407 of obtaining absorbance spectra for one or more of the modified test ligands in the presence of one or more of the nthCYP proteins.

[0097] Any useful values can be employed for m, n, and p. For example and without limitation, m can be selected based on the number of ligands to provide a sufficiently validated model. In some embodiments, m is from 1 to 50 (e.g., from 1 to 10, 1 to 20, 1 to 30, 1 to 40, 2 to 10, 2 to 20, 2 to 30, 2 to 40, 2 to 50, 5 to 10, 5 to 20, 5 to 30, 5 to 40, 5 to 50, 8 to 10, 8 to 20, 8 to 30, 8 to 40, 8 to 50, 10 to 20, 10 to 30, 10 to 40, 10 to 50, 12 to 20, 12 to 30, 12 to 40, 12 to 50, 15 to 20, 15 to 30, 15 to 40, 15 to 50, 18 to 20, 18 to 30, 18 to 40, 18 to 50, 20 to 30, 20 to 40, 20 to 50, 30 to 40, or 30 to 50). For example and without limitation, n can be selected based on the number of CYP proteins to sufficiently characterize rate of metabolism in vivo. In some embodiments, n is from 1 to 10 (e.g., from 1 to 3, 1 to 5, 1 to 8, 2 to 3, 2 to 5, 2 to 8, 2 to 10, 3 to 5, 3 to 8, 3 to 10, 4 to 5, 4 to 8, or 4 to 10). For example and without limitation, ? can be selected based on the number of data points to provide a binding curve for determining binding affinity. In some embodiments, / ? is from 1 to 20 (e.g., from 1 to 3, 1 to 5, 1 to 8, 1 to 10, 1 to 12, 1 to 15, 1 to 18, 2 to 3, 2 to 5, 2 to 8, 2 to 10, 2 to 12, 2 to 15, 2 to 18, 2 to 20, 5 to 8, 5 to 10, 5 to 12, 5 to 15, 5 to 18, 5 to 20, 8 to 10, 8 to 12, 8 to 15, 8 to 18, 8 to 20, 10 to 12, 10 to 15, 10 to 18, 10 to 20, 12 to 15, 12 to 18, 12 to 20, 15 to 18, 15 to 20, or 18 to 20).Systems and devices

[0098] The methods herein can be implemented in any useful manner (e.g., a computer system, a computer readable storage medium, and the like).

[0099] In general, embodiments of the methods herein can be implemented in a computer program, which can take the form of a software component of a suitable hardware platform, for example, a standalone computer, one or more networked computers, network server computers, a handheld device, or the like. Different aspects of the disclosed methods may be implemented in different software modules and executed by one processor or different processors, sequentially or in parallel, depending on how the software is designed. The apparatus on which the program can be executed can include one or more processors, one or more memory devices (such as ROM, RAM, flash memory, hard drive, optical drive, etc.), input / output devices, network interfaces, and other peripheral devices. A computer readable non-transitory media storing the program is also provided.

[0100] Embodiments of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non transitory storage medium for execution by, or to control the operation of, data processing apparatus. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them. Alternatively, or in addition, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus.

[0101] The term “data processing apparatus” refers to data processing hardware and encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can also be, or further include, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). The apparatus canoptionally include, in addition to hardware, code that creates an execution environment for computer programs, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.

[0102] A computer program, which may also be referred to or described as a program, software, a software application, an app, a module, a software module, a script, or code, can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages; and it can be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A program may, but need not, correspond to a file in a file system A program can be stored in a portion of a file that holds other programs or data, e.g., one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files, e.g., files that store one or more modules, sub programs, or portions of code. A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a data communication network.

[0103] The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by special purpose logic circuitry, e.g., an FPGA or an ASIC, or by a combination of special purpose logic circuitry and one or more programmed computers.

[0104] Computers suitable for the execution of a computer program can be based on general or special purpose microprocessors or both, or any other kind of central processing unit. Generally, a central processing unit will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data. The central processing unit and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio orvideo player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device, e.g., a universal serial bus (USB) flash drive, to name just a few.

[0105] Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks.

[0106] To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's device in response to requests received from the web browser. Also, a computer can interact with a user by sending text messages or other forms of message to a personal device, e.g., a smartphone that is running a messaging application, and receiving responsive messages from the user in return.

[0107] Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface, a web browser, or an app through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN) and a wide area network (WAN), e.g., the Internet. The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and serverarises by virtue of computer programs running on the respective computers and having a clientserver relationship to each other. In some embodiments, a server transmits data, e.g., an HTML page, to a user device, e.g., for purposes of displaying data to and receiving user input from a user interacting with the device, which acts as a client. Data generated at the user device, e.g., a result of the user interaction, can be received at the server from the device.

[0108] In some embodiments, a computer system can include: at least one processor; a preparation module, stored in memory and coupled to at least one processor; a build module, stored in memory and coupled to at least one processor; and a validation module, stored in memory and coupled to at least one processor. In turn, each module can be programmed to perform any method described herein.

[0109] In some embodiments, the preparation module is programmed to receive information comprising a first binding affinity ( si) of a first test ligand to a first CYP protein. In some embodiments, the build module is programmed to build one or more 3D models for the first test ligand bound to the first CYP protein. In some embodiments, the validation module is programmed to validate at least one of the one or more 3D models based on said Ksi.

[0110] In some embodiments, the preparation module is programmed to receive information comprising a binding affinityof an mthtest ligand to an nthCYP protein. In some embodiments, the build module is programmed to build one or more 3D models for the mihtest ligand bound to the nthCYP protein. In some embodiments, the validation module is programmed to validate at least one of the one or more 3D models based on a respective / fSI11;n. [0U1] A non-transitory computer readable storage medium can include a computer readable program. In turn, the computer readable program when executed on a computer causes the computer to perform any method described herein.

[0112] In some embodiments, the computer readable program when executed on a computer causes the computer to assess and / or reducing binding affinity of a first test ligand to a first CYP protein, each prediction comprising causing the computer to perform the steps of receiving information comprising a first binding affinity ( fsi) of a first test ligand to a first CYP protein; one or more 3D models for the first test ligand bound to the first CYP protein; and validate at least one of the one or more 3D models based on said / Li .

[0113] In some embodiments, the computer readable program when executed on a computer causes the computer to assess and / or reducing binding affinity of a first test ligand to a first CYPprotein, each prediction comprising causing the computer to perform the steps of: receiving information comprising a binding affinitysm nof an 777thtest ligand to an 77thCYP protein; one or more 3D models for each 777thtest ligand bound to each 77thCYP protein; and validate at least one of the one or more 3D models based on said / fsm,n

[0114] In certain implementations, the methods described herein can be used to evaluate and / or modify drug-drug interactions. For example, the methods described herein can be used to reduce time dependent inhibition (TDI), a phenomenon that can be encountered in drug metabolism studies and is a common mechanism leading to drug-drug interactions. In TDI, a CYP reacts with a drug, but in some cases, the metabolized drug is sufficiently reactive to react again with the CYP forming a covalent bond. This can reduce (e.g., destroy) the ability of the CYP to function. Over time, as more of the drug is metabolized, more CYP protein gets destroyed and an irreversible time dependent inhibition of that CYP results. The techniques described herein can mitigate this problem because, just as reducing the binding affinity of a ligand slows down the rate it gets metabolized, the reduced binding affinity can slow down the rate at which the CYP is irreversibly inhibited. A sufficient reduction of the binding affinity and the rate can reduce (e.g., stop) the TDI.

[0115] While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or on the scope of what may be claimed, but rather as descriptions of features that may be specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially be claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

[0116] Similarly, while operations are depicted in the drawings and recited in the claims in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing maybe advantageous. Moreover, the separation of various system modules and components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0117] Particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. As one example, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous.EXAMPLESExample 1: CYP3A4 modeling

[0118] A congeneric series of ligands was evaluated with a 3D model for CYP3A4, e.g., as seen in FIG. 5A-5B. The data in this figure demonstrate the validation of one of the 3D models. Each of the circle symbols is one ligand. As can be seen, there are many ligands which together provide strong validation that the model is able to correctly predict the binding affinity of a ligand to the CYP. FIG. 5B shows the summary statistics for the plot in FIG. 5A. This summary allows one to produce a number of values which can be used to quantitatively rank the validation of various models. For example, FIG. 5B shows the root-mean-square error (RMSE) of all the predicted changes in binding affinity (AAG) across all pairs of ligands that were used to validate the model. One can rank the quality of validation of various models by ordering them by this RMSE. Similarly, there is the 7?2, which is the square of the correlation coefficient. This is another measure of the quality of the model validation and can be used to rank various models.Example 2: CYP2D6 modeling

[0119] A congeneric series of ligands was evaluated with a 3D model for CYP2D6, e.g., as seen in FIG. 6A-6B.

[0120] FIG. 6A shows the 3D structure of a validated CYP2D6 model. This model was then used to rationally introduce changes to reduce binding affinity. Here, however, initially testedmolecules binding to the CYP acted as inhibitors, rather than substrates. Inhibition is an undesirable property because it can cause dangerous side effects to the patient (e.g., it renders the CYP unable to metabolize other drugs).

[0121] In FIG. 6B, we show proposed changes to the molecule in order to reduce binding to the CYP, which also should reduce inhibition of the CYP. This is a successful demonstration that a model, validated by using binding affinity data, can then be used to predict new compounds which bind weaker to the CYP. For example, introduction of a single nitrogen atom in the aromatic ring provided a dramatic reduction in binding affinity, as seen by comparing Compound 1 with Compound 3.Example 3: Modeling for multiple isoforms of CYP

[0122] Binding affinity may be modeled for a plurality of isoforms of CYP, e g., as seen in FIG. 7. As can be seen, the same set of ligands can be used to produce a validated model for a 3A4 isoform and another model for a different 2D6 isoform. Such an approach can be useful as a drug may be metabolized by more than one isoform, and such models can be employed to understand metabolism of any number of ligands by any number of isoforms.Example 4: Modeling metabolism of erythromycin versus azithromycin

[0123] Even minor structural differences between two compounds can contribute to significant differences in metabolism for such compounds. As seen in FIG. 8, the structural differences between azithromycin and erythromycin is quite minor. In FIG. 10, the site of metabolism on erythromycin is shown. As the differences between erythromycin and azithromycin are far from the site of metabolism, the changes between the two molecules must not be altering the reactivity of this site of metabolism, rather what must be affected is the change in binding affinity of azithromycin to CYP3A4 compared to erythromycin to CYP3A4. Yet that minor difference can contribute to a 45-fold increase in half-life. In turn, that increase in half-life can allow for a single pill to be an effective course of treatment for some infections when employing azithromycin, as compared to a multidose regime for erythromycin having a shorter half-life.

[0124] A public crystal structure of erythromycin bound to CYP3A4 is provided as PDB ID No. 2V0M. This structure was employed as a starting point to measure the difference in CYP3A4 binding affinity for erythromycin versus azithromycin. FIG. 9 highlights the commonatoms and unique atoms between the two molecules. As can be seen, erythromycin includes a carbonyl group, which is converted to a tertiary amine in azithromycin.

[0125] The change in binding affinity was computationally measured by using Free Energy Perturbation (FEP). In FEP, one can mutate from one molecule to another similar molecule and predict the relative change in binding affinity that this change produces. FEP reports that mutating erythromycin to azithromycin reduces the binding affinity by 0.69 ± 0.19 kcal / mol.

[0126] Furthermore, the presence of the tertiary amine (in azithromycin) is protonated at physiological pH. By changing the nitrogen atom (N) in azithromycin to a protonated form (NH+), FEP predicts the binding affinity to be additionally reduced by 8.42 ± 0.15 kcal / mol.

[0127] By combining these changes from erythromycin to azithromycin and from nonprotonated to protonated azithromycin, computational methods predict an enormous reduction in binding affinity to CYP3A4 for azithromycin, as compared to erythromycin. As can be seen, a model of erythromycin bound to CYP3A4 can be rationally used to predict binding affinity for a different compound, in which the different compound exhibited a predicted reduction in binding affinity to CYP. In turn, such modeling provides evidence that a predicted reduction in binding affinity can lead to an increase in half-life.Example 5: Dominant binding mode for initiating CYP catalysis

[0128] As described in Example 4, the FEP predicted a significant reduction in binding affinity between erythromycin and azithromycin. A +9 kcal / mol reduction in binding affinity should correspond to at least a 3000-fold reduction in the rate of metabolism. Yet, the observed reduction is only 45-fold (when comparing the half-life of erythromycin and azithromycin). In order to understand this discrepancy, the binding modes of erythromycin and azithromycin to 3A4 were explored.

[0129] The crystal structure of erythromycin bound to 3A4 showed a binding mode that appears different than the one responsible for metabolism. The dominant metabolite for erythromycin includes N-desmethyl-erythromycin, in which the difference between erythromycin and N-desmethyl-erythromycin is the removal of a methyl group (FIG. 10). In the crystal structure of erythromycin bound to 3A4, that group is distant from the heme of 3A4 (e.g., about 17 A from the catalytic heme). For removal of the methyl group by 3A4, that group should exist in at least another binding mode in which the methyl group is in proximity to the heme.

[0130] Without wishing to be limited by mechanism or theory, another dominant binding mode may be present when 3A4 binds to azithromycin, as compared to the initial binding mode between 3A4 and erythromycin. For example, a dominant binding mode may be responsible for the ejection of the water from the heme (the first step in the CYP catalytic cycle). When erythromycin is mutated to azithromycin, another less-favorable dominant binding mode may be adopted. While this binding mode may be poorer than the original binding mode (e.g., providing a 45-fold reduction in half-life), the original erythromycin binding mode is predicted to be far poorer with a predicted 3000-fold reduction in half-life.

[0131] Therefore, to reduce the half-life further, one can model this new binding mode and propose yet another change to reduce the binding affinity further. Further structural modifications to compounds can be provided until a satisfactory half-life is obtained. If another dominant binding mode is suspected, then further new binding modes can be modeled with, if desired, further structurally modified compounds. As seen in Example 1 herein, such models can be successfully produced. In Example 1, the binding affinity was obtained using IC50 data, which is typically measured for inhibitors. For metabolites, other values can be obtained (e.g., by use of spectroscopic binding data).

Claims

CLAIMS1. A method of assessing and / or reducing binding affinity of a first test ligand to a first cytochrome P450 (CYP) protein, the method comprising: determining a first binding affinity ( / s]) of the first test ligand to the first CYP protein; building one or more three-dimensional (3D) models for the first test ligand bound to the first CYP protein; and validating at least one of the one or more 3D models based on said Ksl, wherein the at least one 3D model provides a predicted binding affinity of the first test ligand by the first CYP protein.

2. The method of claim 1, further comprising (e.g., prior to said determining): obtaining absorbance spectra for the first test ligand at one or more ligand concentrations in the presence of the first CYP protein, wherein an absorbance spectrum is obtained at each ligand concentration.

3. The method of claim 2, wherein the absorbance spectra provide said KS1and / or a first Hill coefficient (HHI); and / or wherein the absorbance spectra comprise a Type I spectra, a reverse Type I spectra, or a Type II spectra.

4. The method of claim 1 or 2, further comprising (e.g., after said validating): predicting one or more modifications to the first test ligand to provide a modified first test ligand having lower binding to the first CYP protein, as compared to binding of the first test ligand to the first CYP protein.

5. The method of claim 4, wherein the one or more modifications comprises inclusion of one or more heteroatoms, replacement of hydrogen with halo, replacement of amino with carbonyl, and / or a positional isomer.

6. The method of claim 4, further comprising (e.g., after said predicting): providing the modified first test ligand; anddetermining a modified first binding affinity of the modified first test ligand to the first CYP protein.

7. The method of claim 6, whereinis greater than Ksl.

8. The method of claim 6, wherein the modified first test ligand comprises a longer half-life ( / 1 / 2) than the first test ligand.

9. The method of claim 6, further comprising (e.g., prior to said determiningobtaining absorbance spectra for the modified first test ligand at one or more ligand concentrations in the presence of the first CYP protein, wherein an absorbance spectrum is obtained at each ligand concentration.

10. The method of any one of claims 6-9, wherein said building comprises predicting a modified binding mode for the modified first test ligand to the first CYP protein.

11. The method of claim 10, wherein the modified binding mode differs from a first binding mode for the first test ligand to the first CYP protein.

12. The method of claim 10, wherein said predicting the modified binding mode comprises: comparing K^odto KS1to determine a difference betweenand Ksl; receiving a template ligand-biomolecule structure, the template ligand-biomolecule structure comprising a template ligand docked in the binding site of the biomolecule, wherein the template ligand comprises the first test ligand, and wherein the biomolecule comprises the first CYP protein; comparing a pharmacophore model of the template ligand to a pharmacophore model of a target ligand, wherein the target ligand comprises the modified first test ligand; overlapping the pharmacophore model of the target ligand with the pharmacophore model of the template ligand while the template ligand is in the binding site of the biomolecule; andpredicting the docked position of the target ligand in the binding site of the biomolecule based on a position of the pharmacophore model of the target ligand when overlapped with the pharmacophore model of the template ligand, wherein the docked position comprises the modified binding mode.

13. The method of any one of claims 1-12, wherein said building comprises predicting a docked position of the first test ligand in a binding site of the first CYP protein.

14. The method of claim 13, wherein said predicting the docked position comprises: receiving a template ligand-biomolecule structure, the template ligand-biomolecule structure comprising a template ligand docked in the binding site of the biomolecule, wherein the biomolecule comprises the first CYP protein; comparing a pharmacophore model of the template ligand to a pharmacophore model of a target ligand, wherein the target ligand comprises the first test ligand; overlapping the pharmacophore model of the target ligand with the pharmacophore model of the template ligand while the template ligand is in the binding site of the biomolecule; and predicting the docked position of the target ligand in the binding site of the biomolecule based on a position of the pharmacophore model of the target ligand when overlapped with the pharmacophore model of the template ligand.

15. The method of any one of claims 1-14, wherein said validating comprises computing a free energy calculation for the first test ligand in a binding site of the first CYP protein.

16. The method of claim 15, wherein said computing the free energy calculation comprises free energy perturbation (FEP), molecular mechanics with generalized Born and surface area solvation (MM / GBSA), molecular mechanism with Poisson-Boltzmann and surface area solvation (MM / PBSA), thermodynamic integration, or metadynamics.

17. The method of any one of claims 1-16, wherein AS1is determined based on absorbance within a range from about 300 to 405 nm, about 380 to 390 nm, about 390 to 420 nm, or about420-440 nm; or wherein Kslis determined based on a difference in absorbance within a range from about 390 nm to 420 nm, as compared to a baseline absorbance within a range from about 380 to 390 nm.

18. The method of any one of claims 1-17, wherein Kslis indicative of a transition from a hexacoordinate, low spin iron complex of the first CYP protein to a pentacoordinate, high spin complex of the first CYP protein.

19. The method of any one of claims 1-18, wherein the first test ligand comprises a congeneric ligand from a plurality of test ligands.

20. The method of any one of claims 1-19, wherein the first CYP protein is selected from the group consisting of CYP1A1, CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP2J2, CYP3A4, CYP3A5, CYP3A7, and CYP19A1, or a modified form thereof, and / or a fragment thereof.21 . The method of any one of claims 1-20, further comprising: determining a second binding affinity (Ks2) of the first test ligand to a second CYP protein that is different than the first CYP protein; building one or more three-dimensional (3D) models for the first test ligand bound to the second CYP protein; validating at least one of the one or more 3D models based on said Ks2, wherein the at least one 3D model provides a predicted binding affinity of the first test ligand by the second CYP protein; and optionally predicting one or more modifications to the first test ligand to provide a modified first test ligand having lower binding to the first CYP protein and to the second CYP protein, as compared to binding of the first test ligand to the respective first or second CYP protein.

22. The method of any one of claims 1-21, further comprising: determining a first Hill coefficient (AHI) of the first test ligand to the first CYP protein.

23. A method of assessing and / or reducing binding affinity of an m number of test ligands to an n number of cytochrome P450 (CYP) proteins, the method comprising: obtaining absorbance spectra for each 777thtest ligand at a number of ligand concentrations in the presence of each 77thCYP protein, wherein the absorbance spectra comprise an m x n x p number of absorbance spectra obtained for each wthtest ligand at each plhconcentration with each nihCYP protein; determining an mxn number of binding affinities ( / fsm,n) of each 777thtest ligand to each / zthCYP protein based on the m n x p number of absorbance spectra; building one or more three-dimensional (3D) models for each 777thtest ligand bound to each / 1thCYP protein; validating at least one of the one or more 3D models based on said 7fsm n, wherein the at least one 3D model provides a predicted binding affinity of at least onetest ligand by at least one / ?thCYP protein; and predicting one or more modifications to at least one 777thtest ligand to provide a modified test ligand having lower binding (e.g., characterized by a modified binding affinity ofto one or more 77thCYP proteins, as compared to binding of the at least one 777thtest ligand to corresponding 77thCYP protein (e.g., whereinis greater than / <sm n), wherein each of 777, n, and p is, independently, an integer of one or more.

24. A computer system comprising: at least one processor; a preparation module, stored in memory and coupled to at least one processor, wherein the preparation module is programmed to receive information comprising a first binding affinity (^si) of a first test ligand to a first cytochrome P450 (CYP) protein; a build module, stored in memory and coupled to at least one processor, wherein the build module is programmed to build one or more three-dimensional (3D) models for the first test ligand bound to the first CYP protein; and a validation module, stored in memory and coupled to at least one processor, wherein the validation module is programmed to validate at least one of the one or more 3D models based on said Ksi.

25. A non-transitory computer readable storage medium comprising a computer readable program, wherein the computer readable program when executed on a computer causes the computer to assess and / or reduce binding affinity of a first test ligand to a first cytochrome P450 (CYP) protein, each prediction comprising causing the computer to perform the steps of: receiving information comprising a first binding affinity (Ksl) of a first test ligand to a first cytochrome P450 (CYP) protein; one or more three-dimensional (3D) models for the first test ligand bound to the first CYP protein; and validate at least one of the one or more 3D models based on said Ksl.

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