Digital assistant to support product development

Abstract

To provide a computer implemented method and an apparatus to facilitate product development, such as pharmaceutical product development.SOLUTION: To start the development process, a user selects the preferred dosage form, defines a target profile and enters key characteristics of the active ingredient. ID of the active ingredient is not necessarily disclosed. The apparatus processes the provided data and calculates key parameters of the AI. Similar key parameters are calculated for common pharmaceutical excipients and stored in the apparatus. The apparatus then selects all relevant excipients and suggests a suitable manufacturing process. Combinations of active ingredients and excipients qualify as drug formulation if the predicted properties comply with the defined target profile. The following aspects can be considered: solubility and permeability of the active ingredient, dissolution of the active ingredient, probability to pass the content uniformity criteria, flowability of the powder blend, tabletability of the powder blend, mechanical strength and size of the tablet, compatibility of active ingredients and excipients etc.SELECTED DRAWING: None

Description

[Technical field]

[0001] The present invention relates to a computer-implemented method, apparatus and system for identifying suitable formulations for product development.The present invention further relates to a computer program element. [Background technology]

[0002] The development of high-quality products, such as pharmaceuticals, presupposes a thorough understanding of the active ingredients, excipients and manufacturing processes. Today, the development process is mainly experimental and driven by the expertise and intuition of the individual inventors. Thus, for example, formulation development is often time-consuming and expensive. Furthermore, there is a high risk of failure and, in the worst case, the produced pharmaceutical product may not comply with current quality and regulatory standards. Similar considerations apply to the development of cleaning agents, cosmetics, dietary supplements, fungicide formulations, herbicide formulations, insecticide formulations or cleaning agents. Summary of the Invention

[0003] During product development, there may be a need to assist and support creators.

[0004] The object of the invention is solved by the subject matter of the independent claims, further embodiments are incorporated in the dependent claims. It is noted that the aspects of the invention described below also apply to computer-implemented methods, apparatus, systems and computer program elements.

[0005] A first aspect of the present invention provides a computer-implemented method for identifying suitable formulations for product development, the method comprising: a) via an input channel; Dosage form; · Target Product Profile, TPP, containing minimum product requirements; ·Main physicochemical properties of the active ingredient (AI); receiving user input defining b) calculating, by a processor, key parameters of the AI ​​relevant to development of the dosage form based on the key physicochemical properties of the AI; c) recalculating, by the processor, the key parameters of the AI ​​in combination with one or more excipients selected from an excipient database; d) selecting, by the processor, from the one or more selected excipients, at least one promising excipient that can improve the key parameters of the AI; e) suggesting, by said processor, said AI-based manufacturing process, said selected at least one promising excipient, and said dosage form; f) predicting, by said processor, product characteristics based on said proposed manufacturing process, a combination of said AI with said at least one selected promising excipient, and said dosage form; g) determining, by the processor, whether the predicted product characteristics comply with a TPP defined by the user; h) identifying, by the processor, an appropriate formulation based on a combination of the AI ​​and the at least one selected promising excipient, the proposed manufacturing process, and the dosage form if the predicted product characteristics are determined to comply with the TPP defined by the user.

[0006] A further aspect of the present invention provides an apparatus for identifying a suitable formulation for product development, the apparatus comprising: Input units; A processing unit, a) via an input channel; Dosage form; · Target Product Profile, TPP, containing minimum product requirements; ·Main physicochemical properties of the active ingredient (AI); receiving user input defining b) calculating, by a processor, key parameters of the AI ​​relevant to development of the dosage form based on the key physicochemical properties of the AI; c) recalculating, by the processor, the key parameters of the AI ​​in combination with one or more excipients selected from an excipient database; d) selecting, by the processor, from the one or more selected excipients, at least one promising excipient that can improve the key parameters of the AI; e) suggesting, by said processor, said AI-based manufacturing process, said selected at least one promising excipient, and said dosage form; f) predicting, by said processor, product characteristics based on said proposed manufacturing process, a combination of said AI with said at least one selected promising excipient, and said dosage form; g) determining, by the processor, whether the predicted product characteristics comply with a TPP defined by the user; h) identifying, by the processor, a suitable formulation based on a combination of the AI ​​and the at least one selected promising excipient, the proposed manufacturing process, and the dosage form if the predicted product characteristics are determined to conform to the user-defined TPP; A processing unit configured to perform the steps of:

[0007] In other words, a computer-implemented method and apparatus is proposed that allows the formulater to develop a robust drug formulation in a cost-effective and time-efficient manner. To begin the development process, the user selects a preferred dosage form (e.g., granules, pellets, capsules, tablets, etc.), defines a target profile (e.g., amount of active ingredient per unit, dosage form size, dosage form mechanical strength, desired release behavior, etc.), and inputs key characteristics of the active ingredient (e.g., true density, particle size distribution data, bulk and tapped density, angle of repose, compressibility and moldability profile, etc.). The identity of the active ingredient (e.g., chemical name or structure) is not necessarily disclosed.

[0008] In step b), the device processes the provided data by normalizing and scaling the data and calculates the important parameters of the AI ​​(e.g. particle size, powder density, powder flowability, and tabletability). In other words, the device evaluates the processability of the active ingredient without any excipients.

[0009] For example, 14 parameters (i.e., d10 value, d50 value, d90 value, distribution span, bulk density, tapped density, compressibility index, Hausner ratio, angle of repose, compaction pressure at 0.15 porosity, tensile strength at 0.15 porosity, tensile strength at 100 MPa compaction pressure, tensile strength at 150 MPa compaction pressure, and tensile strength at 250 MPa compaction pressure) may be used to evaluate the processability of the active ingredient. All parameters can be scaled from 0 to 10, with 0 meaning "poor", 10 meaning "excellent" and 5 being the acceptable value for direct compression.

[0010] The parameters can be classified into the following categories: processability (average values ​​of all parameters), particle size (average values ​​of d10, d50, d90 and distribution span), powder density (average values ​​of bulk and tapped density), powder flow (average values ​​of compressibility, Hausner ratio and angle of repose) and tabletability (average values ​​of compaction pressure at 0.15 porosity, tensile strength at 0.15 porosity, tensile strength at 100 MPa compaction pressure, tensile strength at 150 MPa compaction pressure and tensile strength at 250 MPa compaction pressure).

[0011] The radar chart can be used to quickly identify the strengths (values ​​of 5 or more) and weaknesses (values ​​of less than 5) of an active ingredient. In general, it can be concluded that the processability improves as the area enclosed by the curve increases. Direct compression is possible when processability, powder flow and tabletability are 5 or more; dry granulation (e.g., roller compaction, slugging, etc.) is possible when the tabletability parameters are 5 or more; wet granulation (e.g., fluid bed or high shear granulation) is always possible. An exemplary radar chart is shown in FIG. 3D.

[0012] In step c), the device predicts the key properties of the AI ​​when combined with common pharmaceutical excipients by applying blending rules; the device then processes the data by normalizing and scaling the data to calculate the key parameters of the AI ​​when combined with common pharmaceutical excipients (e.g., particle size, powder density, powder flowability, and tabletability). In other words, the device predicts the processability of a powder blend (i.e., a combination of an active ingredient and a filler / binder). The user needs to input the properties of the active ingredient, select an excipient or excipient blend from a list, and input the weight fraction of the active ingredient.

[0013] For example, the properties of powder blends (i.e. particle size distribution, bulk density, tapped density, angle of repose, compressibility and moldability profiles) are estimated from single component data by applying rules of mixtures. To estimate the particle size distribution of a powder blend, the cumulative size distributions of the individual components are reconstructed from their d10, d50 and d90 values ​​(particles are assumed to be spherical and log-normally distributed). The cumulative size distribution of the powder blend is then derived from the volume-weighted arithmetic average of the individual curves.

[0014] For example, a mixing rule can use the following equation to predict the particle size distribution of a powder blend: F(x) is the cumulative distribution function of particle size, y i is the volume fraction of the component.

[0015]

number

[0016] The bulk density of the powder blend is calculated from the weighted arithmetic average of the individual values; more weight is given to components with smaller bulk densities. The tapped density of the powder blend is calculated from the weighted arithmetic average of the individual values; more weight is given to components with larger tapped densities. The angle of repose of the powder blend is calculated from the weighted arithmetic average of the individual values; more weight is given to components with larger angles of repose.

[0017] For example, a mixing rule can calculate the bulk density of a powder blend using the following formula: D bulk,1 and D bulk,2 represent the smaller and larger bulk densities, x1 and x2 represent the weights of the components with smaller and larger bulk densities, and WF is a weighting factor that depends on the grain size.

[0018]

number

[0019] For example, a mixing rule can calculate the tap density of a powder blend using the following formula: D tap,1 and D tap,2 represent the smaller and larger tap densities, x1 and x2 represent the weights of the components with smaller and larger tap densities, and WF is a weighting factor that depends on the particle size.

[0020]

number

[0021] A similar approach can be used to predict the angle of repose of a powder blend.

[0022] For example, a mixing rule can use the following formula to calculate the angle of repose of a powder blend: where α1 and α2 represent the smaller and larger angles of repose, x1 and x2 represent the weights of the components with larger and smaller angles of repose, and WF is a weighting factor depending on particle size.

[0023]

number

[0024] The compressibility profile of the powder blend is derived from the volume-weighted arithmetic average of the individual profiles; a least-squares fitting is performed to determine the compaction pressure at zero porosity and the compressibility resistance.

[0025] For example, a rule of mixture can calculate the compressibility profile of a powder blend using the following equation: ε(P) is the tablet porosity at compaction pressure P, and y i is the volume fraction of the component.

[0026]

number

[0027] The moldability profile of the powder blend can be derived from the volume weighted geometric mean of the individual profiles; least squares fitting can be performed to determine the tensile strength at zero porosity and bonding ability.

[0028] For example, a rule of mixture can calculate the compactability profile of a powder blend using the following formula: where T(ε) is the tensile strength of a tablet at porosity ε and y i is the volume fraction of the component:

[0029]

number

[0030] In step d), the device identifies suitable excipients.

[0031] For example, the machine calculates the weight fraction of the active ingredient based on a given dose and tablet weight; it subtracts 7% from the tablet weight to accommodate disintegrants and lubricants. In the next step, the machine predicts the processability of all possible active ingredient-excipient combinations. The properties of the powder blends (i.e. particle size distribution, bulk density, tapped density, angle of repose, compressibility and compactability profiles) are estimated from the single component data by applying rules of mixtures.

[0032] Thus, 14 parameters (i.e., d10, d50, d90, distribution span, bulk density, tapped density, compressibility index, Hausner ratio, angle of repose, compaction pressure at 0.15 porosity, tensile strength at 0.15 porosity, tensile strength at 100 MPa compaction pressure, tensile strength at 150 MPa compaction pressure, and tensile strength at 250 MPa compaction pressure) may be used to evaluate the processability of powder blends. All parameters can be scaled from 0 to 10, with 0 meaning "poor", 10 meaning "excellent" and 5 being acceptable for direct compression.

[0033] The parameters can be classified into the following categories: processability (average values ​​of all parameters), particle size (average values ​​of d10, d50, d90 and distribution span), powder density (average values ​​of bulk and tapped density), powder flow (average values ​​of compressibility, Hausner ratio and angle of repose) and tabletability (average values ​​of compaction pressure at 0.15 porosity, tensile strength at 0.15 porosity, tensile strength at 100 MPa compaction pressure, tensile strength at 150 MPa compaction pressure and tensile strength at 250 MPa compaction pressure).

[0034] Radar charts can also be used to quickly identify strengths (values ​​above 5) and weaknesses (values ​​below 5) of a powder blend. In general, it can be concluded that processability improves as the area enclosed by the curve increases. Direct compression is possible when processability, powder flow and tabletability are above 5; dry granulation (e.g. roller compaction, slugging, etc.) is possible when the tabletability parameters are above 5; wet granulation (e.g. fluid bed or high shear granulation) is always possible.

[0035] Once all combinations have been tested, the instrument classifies the tested excipients and excipient blends according to their performance. For this purpose, a weighted average of all 14 parameters is calculated. Tabletability and powder flow are more important, particle size and powder density are less important.

[0036] In step e), the device suggests a suitable manufacturing process and selects the most suitable excipient or excipient blend.

[0037] Taking direct compression as an example, according to the manufacturing classification system, if a material meets certain requirements regarding particle size and shape, blend uniformity, powder flow, powder density, tableting ability, and mechanical strength of the compact, direct compression is feasible.

[0038] For example, in direct compression, excipients and excipient blends with poor flowability and / or tabletability (values ​​less than 5) are screened out. All excipients and excipient blends are rated on a scale from zero ("not suitable") to five stars ("most suitable"). "Five stars" indicates the best performing excipient or excipient blend, "one star" indicates the worst performing excipient or excipient blend, and "zero stars" means that the excipient or excipient blend is not suitable for the direct compression process.

[0039] In step f), if the predicted product properties fit the defined target profile, the combination of active ingredient and excipients is qualified as a drug formulation. The following aspects can be considered: solubility and permeability of the active ingredient, solubility of the active ingredient, probability of passing the content uniformity criteria, flowability of the powder blend, tabletability of the powder blend, mechanical strength and size of the tablet, compatibility of the active ingredient and excipients, etc.

[0040] Using the tabletability of a powder blend as an example, the properties of the powder blend (i.e., compressibility and compactability profiles) are estimated from the single component data by applying rules of mixtures: the compressibility profile of the powder blend is obtained from the volume-weighted arithmetic mean of the individual profiles, and the compactability profile of the powder blend is obtained from the volume-weighted geometric mean of the individual profiles.

[0041] Compressibility describes the ability of a powder to reduce in volume as a result of applied pressure and is represented by a plot of porosity versus compression pressure. Moldability describes the ability of a powder to be converted into a tablet of a particular strength during agglomeration and is represented by a plot of tensile strength versus porosity. Tabletability describes the ability of a powder to be converted into a tablet of a particular strength as a result of applied pressure and is represented by a plot of tensile strength versus compression pressure.

[0042] Compaction pressure is calculated from the applied force and the cross-sectional area of ​​the punch tip. For cylindrical tablets, compaction pressure can be calculated using the following formula, where P is the compaction pressure, F is the applied force, and D is the tablet diameter:

[0043]

number

[0044] The porosity can be calculated from the tablet weight (m), the true density of the tablet (ρ), and the tablet volume (V):

[0045]

number

[0046] Tensile strength is a measure of the mechanical strength of a tablet taking into account its shape and size. For cylindrical tablets, the tensile strength can be calculated by the following formula: where σ is the tensile strength, F is the breaking load, D is the tablet diameter and H is the tablet thickness:

[0047]

number

[0048] For example, by using the methods described above, the device can predict whether the required tensile strength of a tablet can be achieved within the compaction pressure limits provided.

[0049] In step h), the device calculates a starting formulation using the selected filler-binder combination.

[0050] A superdisintegrant is added, the amount of which depends on the selected filler-binder combination. For example, if the filler-binder combination already contains a disintegrant (e.g., a ready-to-use excipient), the amount of superdisintegrant is reduced accordingly. If the tabletability of the powder blend is medium (values ​​below 6), a superdisintegrant with binding properties (e.g., Kollidon® CL-SF) is selected; otherwise, a normal superdisintegrant (e.g., Kollidon® CL-F) is added. Sodium stearyl fumarate is added as a lubricant; the amount of which depends on the selected filler-binder combination. In contrast to magnesium stearate, sodium stearyl fumarate does not cause excessive lubrication and is less incompatible with the active ingredient. Once the exact amounts of superdisintegrant and lubricant are determined, the amount of the filler-binder combination is recalculated.

[0051] As described below, particularly with respect to the exemplary embodiment of FIG. 4, if a formulation has not been identified, the device may suggest further technical measures (e.g., grinding or micronization, addition and processing of excipients, etc.) to optimize the properties of the active ingredient. Alternatively, the device may suggest adjusting the target profile or selecting a different dosage form. In a next step, the user can re-enter the main characteristics of the active ingredient, redefine the target profile, or select a different dosage form. The development process can be started over and the device can re-evaluate the data. If at least one formulation has been identified, the user prepares the formulation in the laboratory and characterizes the resulting product. If the experimental results (e.g., content uniformity, dissolution profile, tablet mechanical strength, etc.) fit the target profile, the development process ends. If not, the device suggests optimizing the properties of the active ingredient, adjusting the target profile, or selecting a different dosage form. In the last step, the user may print the formulation, download related information (e.g., quality, regulatory and technical documents), and order a product sample. Additionally, users may provide feedback (e.g., regarding usability, information content, formulation results, etc.) that is used to improve the user experience and the device itself. The demand from authorities to comply with a quality by design approach is ideally realized by using this device. Besides traditional tablet formulations, the device can also assist in the development of other dosage forms such as: capsules, including hard capsules and soft capsules, chewing gums, creams, emulsions, including emulsion concentrates and microemulsions, foams, sprays, gels, sticks, granules, gummies, implants, ointments, pastes, pellets, including coated pellets, powders, solutions, including injection solutions, suppositories, suspensions, including concentrates, extended release formulations, tablets, including buccal tablets, chewable tablets, coated tablets, detergent tablets, dishwashing tablets, effervescent tablets, lozenges, orodispersible tablets, vaginal tablets, therapeutic patches.

[0052] The manufacturing techniques covered by the equipment include coating, direct compression, granulation, extrusion, pelletizing, 3D printing, emulsification, dispersion, blending, dissolving, encapsulation, prilling, roller compaction, spray drying and various drying techniques such as other compounding techniques. These techniques can be applied in batch mode or continuously. The active ingredients can be selected from the following groups: pharmaceutical, nutritional, cosmetic, agrochemical or cleaning agents.

[0053] This can help users (e.g., businesses) identify appropriate formulations. The number of laboratory experiments is reduced to an absolute minimum. This will speed up formulation development and save costs.

[0054] According to one embodiment of the present invention, if it is determined that the predicted product characteristics do not conform to the user-defined TPP or that the experimental results obtained after preparing and characterizing the identified suitable formulation do not conform to the user-defined TPP, the method further includes performing at least one of the following steps: proposing at least one alternative technical measure to optimize key parameters of the AI ​​based on the difference between the predicted product characteristics and the user-defined TPP or between the experimental results and the user-defined TPP; proposing to adjust the user-defined TPP based on the difference between the predicted product characteristics and the user-defined TPP or between the experimental results and the user-defined TPP; proposing to select an alternative dosage form based on the difference between the predicted product characteristics and the user-defined TPP or between the experimental results and the user-defined TPP.

[0055] In other words, if a formulation has not been identified, further technical measures (e.g., grinding or micronization, addition and processing of excipients, etc.) are suggested to optimize the properties of the active ingredient. Alternatively or additionally, it is suggested to adjust the target profile or select a different dosage form. The same applies if the experimental results do not fit the target profile. For example, if at least one formulation has been identified, the user prepares the formulation in the laboratory and characterizes the resulting product. If the experimental results (e.g., content uniformity, dissolution profile, tablet mechanical strength, etc.) fit the target profile, the development process is terminated. If not, the system may suggest optimizing the properties of the AI, adjusting the target profile, and / or selecting a different dosage form.

[0056] According to one embodiment of the invention, the at least one additional technical measure comprises at least one of grinding or micronization, and addition of and treatment with excipients.

[0057] According to one embodiment of the invention, if the predicted product characteristics do not fit the user-defined TPP or if it is determined that the experimental results obtained after preparing and characterizing the identified suitable formulation do not fit the user-defined TPP, the method further includes iteratively performing a sequence including: receiving further user input related to different dosage forms, user-redefined TPPs, and / or redetermined key physicochemical characteristics of the AI; performing steps b) to h) until a suitable formulation is identified with product characteristics that fit the user-defined or user-redefined TPP.

[0058] According to one embodiment of the present invention, product development includes at least one of the following: cleaning product development, cosmetic product development, dietary supplement development, formulation development, fungicide formulation development, herbicide formulation development, pesticide formulation development, and detergent development.

[0059] According to one embodiment of the invention, the dosage form comprises at least one of the following: capsules, including hard capsules and soft capsules, chewing gums, creams, emulsions, including emulsion concentrates and microemulsions, foams, sprays, gels, sticks, granules, gummies, implants, ointments, pastes, pellets, including coated pellets, powders, solutions, including injection solutions, suppositories, suspensions, including suspension concentrates, sustained release formulations, tablets, including buccal tablets, chewable tablets, coated tablets, cleaning tablets, dishwashing tablets, effervescent tablets, lozenges, orodispersible tablets, vaginal tablets, and therapeutic patches.

[0060] A user interface (eg, command line, graphical user interface) may be provided to facilitate the user's selection of one of the dosage forms.

[0061] According to one embodiment of the invention, the user-defined TPP includes at least one of the following: amount and / or concentration of the active ingredient; size, volume and / or weight of the dosage form; mechanical and / or rheological properties of the dosage form; release profile of the active ingredient; other application-related parameters; compatibility and stability; and other manufacturing-related properties.

[0062] These are general examples of user-defined TPPs that are applicable to any type of dosage form. A user interface (e.g., command line, graphical user interface) may be provided to facilitate the user to define the TPP. This is described below, particularly with respect to the exemplary embodiments of Figures 2 and 3C.

[0063] According to one embodiment of the present invention, the user-defined TPP includes at least one of the following: the amount of AI per unit; the size and / or weight of the dosage form; the mechanical strength of the dosage form; the desired release behavior of the dosage form; the disintegration time of the dosage form; the dissolution profile of the AI; the compatibility of the active ingredient and excipients; the probability of passing the content uniformity criterion; the flowability of the powder blend; the tabletability of the powder blend; the compatibility and stability of the active ingredient and excipients.

[0064] The above user-defined TPPs are particularly applicable to solid oral dosage forms.

[0065] According to one embodiment of the present invention, the user-defined TPP includes at least one of the following: concentration of the AI, volume of the dosage form, rheological behavior and / or viscosity of the dosage form, spreading and / or adhesion of the dosage form, dispersibility and / or volume fraction of the phases, hydrophilicity and / or lipophilicity, release behavior of the dosage form, melting point of the dosage form, dissolution profile of the AI, compatibility and stability of the active ingredient and excipients.

[0066] The above user-defined TPPs are particularly applicable to liquid and semi-solid dosage forms.

[0067] According to one embodiment of the invention, the main physicochemical properties of the AI ​​include at least one of the following: hydrophilicity and / or lipophilicity (e.g., distribution coefficient); melting point; permeability across biological or artificial lipid membranes; solubility in water, solvents, co-solvents and / or bio-relevant media; miscibility with water, solvents, co-solvents and / or bio-relevant media; true density; viscosity; wettability; interfacial and / or surface tension; particle size distribution data; particle morphology, shape and / or aspect ratio; bulk and tapped density; flowability (e.g., angle of repose or flow function coefficient); compressibility and moldability; hygroscopicity; water content (e.g., loss on drying); concentration of impurities; other chemical, physicochemical and / or physical properties; compatibility and stability information.

[0068] The user can select an AI from the AI ​​database, and the main physiochemical properties of the selected AI are also automatically obtained from the AI ​​database. Alternatively, the user can define the main physiochemical properties of the AI ​​through a user interface. This will be described below, particularly with reference to the exemplary embodiments of Figures 2 and 3B.

[0069] According to one embodiment of the present invention, the user-defined TPP includes a dose of the AI ​​per unit and a maximum weight of the dosage form. Step c) further includes calculating weight fractions of the AI ​​and one or more excipients selected from an excipient database based on the dose of the AI ​​per unit and the maximum weight of the dosage form, and predicting properties of the combination of the AI ​​and one or more excipients. Step d) further includes selecting at least one promising excipient from the one or more excipients if the properties of the corresponding mixture meet a predetermined criterion.

[0070] In other words, the drug dose and tablet weight are proposed to be expected as input parameters. Drug and excipient weight fractions are calculated. The properties of all potential drug-excipient mixtures are predicted. Excipients are recommended, for example, in cases where the flowability and tabletability of the corresponding mixture meet predefined criteria (e.g., ≥ 5); in this case, all parameters are scaled from 0 to 10, with 0 meaning "inadequate", 10 meaning "excellent" and 5 being acceptable.

[0071] As the drug dose and tablet weight are specified, for example, the size of the tablet can be controlled within an appropriate range.

[0072] According to one embodiment of the invention, the dosage form comprises a pharmaceutical dosage form.

[0073] In one example, the processing unit is further configured to enable a user to: print the formulation, download relevant information including quality information, regulatory information, safety data, and / or technical documentation, order product samples, and / or provide user feedback including usability, information content, and / or formulation results.

[0074] A further aspect of the present invention provides a system for identifying suitable formulations for product development. The system comprises an apparatus according to any one of the exemplary embodiments and examples described above and below. The system further comprises a web server configured to interact with a user via a web page and / or an application program provided by the web server. The apparatus is configured to provide a graphical user interface (GUI) to the user via the web page and / or the application program.

[0075] A further aspect of the invention provides a computer program element comprising a set of instructions which, when executed on a processor of an apparatus of any one of the exemplary embodiments and examples described above and below, causes the apparatus or system to perform a method of any one of the exemplary embodiments and examples described above and below. [Brief description of the drawings]

[0076] These and other aspects of the invention will be apparent and elucidated further with reference to the embodiments described by way of example in the following description and with reference to the accompanying drawings, in which: [Figure 1] FIG. 1 is a block diagram of an exemplary system for identifying suitable formulations for product development. [Diagram 2] FIG. 1 is a block diagram of an exemplary apparatus for identifying suitable formulations for product development. [Figure 3A] 1 shows an example of a graphical user interface that provides support for product development. [Figure 3B] 1 shows an example of a graphical user interface that provides support for product development. [Figure 3C] 1 shows an example of a graphical user interface that provides support for product development. [Figure 3D] 1 shows an example of a graphical user interface that provides support for product development. [Figure 3E]1 shows an example of a graphical user interface that provides support for product development. [Figure 3F] 1 shows an example of a graphical user interface that provides support for product development. [Figure 3G] 1 shows an example of a graphical user interface that provides support for product development. [Figure 3H] 1 shows an example of a graphical user interface that provides support for product development. [Figure 3I] 1 shows an example of a graphical user interface that provides support for product development. [Figure 4] 3 is a flowchart representing example machine-readable instructions for implementing the apparatus of FIG. 1 and / or FIG. 2.

[0077] It should be noted that the drawings are purely schematic and are not drawn to scale. In the drawings, elements corresponding to elements already described may have the same reference numbers. Examples, embodiments, or optional features, whether or not shown as non-limiting, should not be understood as limiting the claimed invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0078] To assist formulators during product development, a digital assistant is proposed that reduces development time by avoiding unnecessary laboratory experiments, lowers the risk of failure, and ensures compliance with the quality target product profile. In particular, a science-based formulation prediction system is proposed that allows formulators to develop more robust drug formulations. To maintain confidentiality, the disclosed apparatus, system, and method do not require formulators to disclose the identity or structure of the active ingredient. Instead, it is proposed to predict the processability of a hypothetical drug-excipient mixture and calculate a starting formulation depending on the selected dosage form and the defined target profile. The disclosed apparatus, system, and method have the advantage that the number of time-consuming and costly laboratory experiments can be reduced. The disclosed apparatus, system, and method can improve the effectiveness and reduce the risk in the early stages of formulation development.

[0079] 1 is a block diagram of an exemplary system 100 for identifying suitable formulations for product development. Examples of product development include, but are not limited to, the following: development of dietary supplements, development of cosmetics, development of fungicide, herbicide and / or insecticide formulations, development of cleaning and / or detergents, and development of pharmaceuticals. The exemplary system 100 of FIG. 1 includes an apparatus 110 for identifying suitable formulations for product development, one or more electronic devices 120 (e.g., a first electronic device 120a, a second electronic device 120b), a network 130, a web server 140, and a data repository 150.

[0080] The exemplary device 110 of FIG. 1 is a computing device having processing capabilities for identifying an appropriate formulation based on user input defining the dosage form, TTP, and key physicochemical properties of the AI. The illustrated exemplary device 110 may be a server. The server communicates with the exemplary electronic device 120 to authenticate a user of the exemplary electronic device 120, provide user input including the dosage form, TPP including minimum product requirements, and key physicochemical properties of the AI, and transmit the appropriate formulation derived by the exemplary device 110 to the exemplary electronic device 120. The device 110 may be integrated with other components of the system 100 (e.g., web server 140 and / or data repository 150). An embodiment of the device 110 is described in relation to FIG. 2.

[0081] A user (e.g., a company interested in product development) in the system 100 communicates with the exemplary apparatus 110 using the electronic device 120 of the illustrated example, inputs parameters (e.g., dosage form, TPP, key physicochemical properties of AI), and obtains an analysis result of whether these input parameters can generate a product with product characteristics that comply with the user-defined TPP. If the product characteristics are determined to comply with the user-defined TPP, the user can further obtain a suitable formulation derived by the apparatus 110. The electronic device 120 of the example shown in FIG. 1 may be a mobile device, such as a mobile phone. Alternatively, the electronic device may be any type of electronic device that can communicate with the apparatus 110 to develop a product formulation. Further examples of the electronic device 120 may be a personal computer (PC) or a workstation. The electronic device 120 may be a computing device. Alternatively, the electronic device 120 may be a device that does not typically include processing capabilities. For example, the electronic device 120 may be a simple user interface, such as a touch screen, that allows a user to input parameters. The electronic devices 120a, 120b may have an additional processor to connect the device to a network. Examples of electronic devices 120a, 120b include mobile devices and PCs.

[0082] The network 130 of the illustrated example communicatively couples the device 110, the web server 140, the data repository 150, and one or more electronic devices 120. An example of the network 130 is the Internet. Alternatively, the network 130 may be any other type and number of networks. For example, the network 130 may be realized by several local area networks connected to a wide area network. For example, the network 130 may include any combination of wired networks, wireless networks, wide area networks, local area networks, etc.

[0083] 1 is a server that provides web services to enable users of one or more electronic devices 120 to access the exemplary apparatus 110 and identify appropriate formulations for their product developments. The web server 140 can include interfaces that can authenticate users, for example by providing a username and password, interfaces for creating, modifying, and deleting user accounts, for example for the apparatus 110, and interfaces for creating, modifying, and deleting development projects for different product developments.

[0084] Web server 140 may interact with a user via web pages and / or application programs provided by web server 140 to facilitate management of a development project for that user using device 110. Web server 140 may alternatively be replaced by another device (e.g., another computing device) providing any type of interface (e.g., a command line interface, a graphical user interface, etc.). In some systems, device 110 may include an integrated web server unit or may include a separate web server 140.

[0085] 1 is a data store that stores data including, for example, an AI database and an excipient database. Additionally, the data store may include examples of end products including recipes, manufacturing techniques, and properties. Data repository 150 may be any type of data store including a server, database, file, etc. Although system 100 includes data repository 150, some implementations of system 100 may not include data repository 150. For example, functionality of data repository 150 may be integrated into exemplary device 110.

[0086] Figure 2 is a block diagram of an exemplary implementation of the device 110 of Figure 1 for identifying suitable formulations for product development. Product development can include at least one of food supplement development, cosmetic development, fungicide, herbicide and / or insecticide formulation development, cleaning agent and / or detergent development, and pharmaceutical development. The following focuses on pharmaceutical development. However, those skilled in the art will understand that the following discussion can also be applied to other product developments, such as cosmetic development.

[0087] 2 includes an exemplary input unit 10, an exemplary processing unit 20, and an exemplary output unit 30. The device 110 may be a server, but the device may alternatively be any other type of computing device (e.g., a desktop computer, a laptop computer, etc.).

[0088] The exemplary input unit 10 is configured to receive user input, for example, from one or more exemplary electronic devices 120 of Figure 1. The user input defines the main physicochemical properties of the dosage form, the TTP, and the AI.

[0089] Examples of dosage forms include, but are not limited to, capsules, including hard capsules and soft capsules, chewing gums, creams, emulsions, including emulsion concentrates and microemulsions, foams, gels, granules, gummies, implants, ointments, pastes, pellets, including coated pellets, powders, solutions, including injection solutions, suppositories, suspensions, including suspension concentrates, sustained release formulations, tablets, including buccal tablets, chewable tablets, coated tablets, cleaning tablets, dishwashing tablets, effervescent tablets, lozenges, orally dispersible tablets, vaginal tablets, and therapeutic patches.

[0090] General examples of user-defined TPPs applicable to all types of dosage forms include, but are not limited to: amount and / or concentration of the active ingredient; size, volume and / or weight of the dosage form; mechanical and / or rheological properties of the dosage form; release profile of the active ingredient; other application-related parameters; compatibility and stability; and other manufacturing-related properties.

[0091] Specific examples of user-defined TPPs for solid oral dosage forms include, but are not limited to, the following: amount of AI per unit; size and / or weight of the dosage form; mechanical strength of the dosage form; desired release behavior of the dosage form; disintegration time of the dosage form; dissolution profile of the AI; compatibility of the active ingredient and excipients; probability of passing content uniformity criteria; flowability of the powder blend; tabletability of the powder blend; compatibility and stability of the active ingredient and excipients.

[0092] Specific examples of user-defined TPPs for liquid and semi-solid dosage forms include, but are not limited to, the following: concentration of the AI; volume of the dosage form; rheological behavior and / or viscosity of the dosage form; spreading and / or adhesion of the dosage form; dispersibility and / or volume fraction of the phases; hydrophilicity and / or lipophilicity; release behavior of the dosage form; melting point of the dosage form; dissolution profile of the AI; compatibility and stability of the active ingredient and excipients.

[0093] The key physicochemical properties of the AI ​​include at least one of the following: hydrophilicity and / or lipophilicity (e.g., diffusion coefficient); melting point; permeability across biological or artificial lipid membranes; solubility in water, solvents, co-solvents and / or biorelevant media; miscibility with water, solvents, co-solvents and / or biorelevant media; true density; viscosity; wettability; interfacial and / or surface tension; particle size distribution data; particle morphology, shape and / or aspect ratio; bulk and tapped density; flowability (e.g., angle of repose or flow function coefficient); compressibility and moldability; hygroscopicity; moisture content (e.g., loss on drying); concentration of impurities; other chemical, physicochemical and / or physical properties; compatibility and stability information.

[0094] To facilitate the user in selecting and defining these parameters, the example web server 140 of FIG. 1 can be configured to interface with the user via a web page and / or an application program provided by the web server. The web page and / or application program presents a graphical user interface on the display of the electronic device 120 to allow the user to select and define these parameters. For example, the web page and / or application program can provide a first graphical user interface for receiving user credentials for authentication, a second graphical interface for allowing the user to select a desired dosage form from a list of dosage forms, a third graphical user interface for allowing the user to select and define the desired parameters from among a list of TPPs, and a fourth graphical user interface for allowing the user to select and define key physicochemical properties of the AI ​​from among a list of physicochemical properties of the AI. To facilitate the user in defining the relevant parameters, the graphical user interface can highlight certain key parameters associated with the selected dosage form.

[0095] An example of a second graphical user interface is described in relation to Figure 3A. The second graphical user interface of the illustrated example allows the user to select at least one of the following dosage forms: granules / pellets, capsules, and tablets. The second graphical user interface of the illustrated example also allows the user to select an intended release behavior (e.g., immediate release, enteric release, and extended release). The second graphical user interface of the illustrated example also allows the user to specify the characteristics of the dosage form, such as conventional granules, conventional pellets, effervescent granules, etc.

[0096] An example of a third graphical user interface is described with reference to FIG. 3B. The example third graphical user interface illustrated in FIG. 3B allows a user to select an AI from an AI database. In the illustrated example, the AI ​​is related to an active pharmaceutical ingredient (API). Thus, in FIG. 3B, the term "API" is used in place of an AI to indicate that the AI ​​is related to drug development. Alternatively, the third graphical user interface may allow a user to d 10 Value, d 50 Value, d 90 The system may allow input of properties of the API such as density, bulk density, tapped density, angle of repose, true density, compaction pressure required to produce a zero open cell formulation, compression resistance, tensile strength at zero open cell, binding capacity, etc.

[0097] An example of a fourth graphical user interface is described in relation to Figure 3C. The fourth graphical user interface allows a user to define the dose of API per unit, the maximum weight of the dosage form, the volume of the dissolution medium, the amount of API dissolved, the dissolution time of the API, the probability of passing the content uniformity criterion, the tablet tensile strength, and the maximum molding pressure.

[0098] When the user input is received, the exemplary processing unit 20 is configured to calculate key parameters of the AI ​​related to the development of the dosage form based on the key physicochemical properties of the AI, selected from the AI ​​database or defined by the user. The key parameters of the AI ​​are calculated from the key physicochemical properties of the AI ​​by normalizing and scaling the data. The web page and / or application program can provide a fifth graphical user interface to allow the user to view the analysis results.

[0099] An example of a fifth graphical user interface is described in relation to FIG. 3D. The fifth graphical user interface of the illustrated example shows a radar chart of the parameters of the AI ​​calculated from the main physical properties of the API by normalizing and scaling the data. For example, 10 Value(D10), d 50 Value(D50), d 90 The parameters are: density (D90), distribution span (DSP), bulk density (DBU), tapped density (DTA), compressibility index (CPI), Hausner ratio (HAR), angle of repose (AOR), compressibility (CPR), moldability (CMP), tensile strength (TST), etc. Based on these parameters, four important parameters (i.e., particle size, powder density, powder flowability, and tabletability) and a risk analysis score are determined. The risk analysis score indicates the processability of the API using a particular manufacturing technique (e.g., direct compression into tablets). In the example shown in Figure 3D, the API has poor flowability and fairly good tabletability, indicating that excipients are required to compensate for the API's defective properties.

[0100] The processing unit 20 in the example shown in FIG. 2 is also configured to recalculate the main parameters of the AI ​​when combined with one or more excipients selected from the excipient database. The desired excipients are those that can correct the missing properties of the API, i.e., improve the flowability and tabletability in the example shown in FIG. 3D. The outcome of the development process depends not only on the properties and quality of the AI, but also on the careful selection of the excipients. The selection of excipients is based on multiple criteria including availability, compatibility, functionality, price, processability, safety, stability, quality, and regulatory aspects. Therefore, knowledge of relevant drug-excipient combinations is very useful for the formulation developer when selecting suitable excipients. This information may already exist for and be stored in the excipient database and / or the AI ​​database. The web page and / or application program may provide a sixth graphical user interface to allow the user to select ingredients from the ingredient database.

[0101] In addition to excipients that can improve the flowability (e.g., fillers, fillers / binders, glidants, etc.) and tabletability (e.g., binders, fillers, fillers / binders, etc.) of the AI, excipients that can improve the manufacturability of the AI ​​(e.g., lubricants), excipients that can improve the disintegration time of the tablet (e.g., disintegrants), excipients that can increase the solubility of the AI ​​(e.g., surfactants, wetting agents, etc.), or excipients that can modify the release behavior of the tablet, can be considered.

[0102] An example of the sixth graphical user interface is described in relation to FIG. 3F. The sixth graphical user interface of the illustrated example allows a user to select one or more excipients from an excipient database store in the exemplary data repository 150 of FIG. 1. Alternatively, the device 110 may automatically select all excipients in the excipient database. The device 110 then recalculates the parameters of the AI ​​when combined with the one or more selected excipients based on pre-established mixing rules. These mixing rules are specific for each of the parameters and are stored in the device 110.

[0103] The processing unit 20 in the example shown in FIG. 2 is further configured to select at least one promising excipient from the one or more selected excipients that can improve key parameters of the AI. The processing unit 20 can rank the excipients based on their improvements on the key parameters and select one or more top-ranked excipients as promising excipients. The web page and / or application program can provide a seventh graphical user interface to allow the user to select ingredients from the ingredient database.

[0104] 3G, an example of a seventh graphical user interface is described. The seventh graphical user interface of the illustrated example allows the user to see the improvement of the main parameters in a radar chart. The shortcomings of four important parameters (i.e., particle size, powder density, powder flowability, and tabletability) have also been corrected, and the risk analysis score has been improved.

[0105] The processing unit 20 is also configured to suggest a manufacturing process based on the AI, the at least one selected promising excipient, and the dosage form. Knowledge about the relevant drug-excipient combinations and the corresponding manufacturing process may already exist and be stored in the excipient database and / or the AI ​​database. The processing unit 20 can extract this information from the excipient database and / or the AI ​​database and provide a suggestion. The risk analysis score indicates the processability of the AI ​​when combined with the at least one selected promising excipient using the suggested manufacturing technology. For example, in FIG. 3G, it is recommended to use direct compression as the most suitable manufacturing process.

[0106] The processing unit 20 is further configured to predict product characteristics based on the proposed manufacturing process, the combination of the AI ​​with the at least one selected promising excipient, and the dosage form, and determine whether the predicted product characteristics comply with the user-defined TPP.

[0107] The processing unit 20 is further configured to identify a suitable formulation based on the combination of the AI ​​and at least one selected promising excipient, the proposed manufacturing process, and the dosage form if the predicted product characteristics are determined to comply with the user-defined TPP. In addition to excipients that can improve the flowability (e.g., fillers, fillers / binders, glidants, etc.) and tabletability (e.g., binders, fillers, fillers / binders, etc.) of the AI, further excipients that can improve the manufacturability of the AI ​​(e.g., lubricants), excipients that can improve the disintegration time of the tablet, excipients that can increase the solubility of the AI ​​(e.g., surfactants, wetting agents, etc.), or excipients that can modify the release behavior of the tablet, may be included. Knowledge of the relevant drug-excipient combinations and the corresponding manufacturing processes may already exist and be stored in the excipient database and / or the AI ​​database. The web page and / or application program may provide an eighth graphical user interface to allow the user to review the identified formulation.

[0108] An example of an eighth graphical user interface is described in connection with FIG. 3H. The eighth graphical user interface of the illustrated example allows the user to view the components and corresponding percentages used in the formulation. The example formulation illustrated in FIG. 3H has 48% API, 48% Ludipress®, 3% Kollidon® CL, and 1% magnesium stearate. A proposed manufacturing process and related parameters can also be presented to the user to facilitate the user in developing the product.

[0109] However, if it is determined that the predicted product characteristics do not conform to the user-defined TPP, the processing unit 20 may be configured to perform at least one of the following steps: suggesting at least one additional technical measure to optimize key parameters of the AI ​​based on the difference between the predicted product characteristics and the user-defined TPP; suggesting adjusting the user-defined TPP based on the difference between the predicted product characteristics and the user-defined TPP; suggesting selecting a different dosage form based on the difference between the predicted product characteristics and the user-defined TPP. The at least one additional technical measure includes at least one of milling or micronization, adding excipients and processing. The processing unit 20 may be configured to repeatedly perform a sequence including: receiving further user input regarding different dosage forms, user-redefined TPPs, and / or redetermined key physicochemical properties of the AI; performing the steps of the above embodiments and examples of Figures 3A-3H until a suitable formulation is identified with product characteristics that are user-defined or conform to the user-redefined TPP.

[0110] Optionally, the processing unit 20 is further configured to allow the user to order product samples, such as potential excipients and / or other ingredients used in the formulation, print the formulation, and / or download related information, including quality information, regulatory information, safety data, and / or technical documentation. The web page and / or application program may provide a ninth graphical user interface to allow the user to select and order excipients from the excipient database. An example of the ninth graphical user interface is described in connection with FIG. 3I. The ninth graphical user interface allows the user to select and order excipients from the excipient database, such as Ludipress® and / or Kollidon®, which may be used in the formulation. As a further option, a further graphical user interface may be provided to enable the user to provide feedback including, for example, usability, information content, and / or formulation results.

[0111] The apparatus 110 may further comprise an output unit for outputting the analysis result.

[0112] A flowchart depicting exemplary machine-readable instructions for implementing the apparatus 110 of Figure 1 and / or Figure 2 is shown in Figure 4. In this example, the machine-readable instructions include a program executed by a processor, such as processing unit 20 shown in the exemplary apparatus of Figure 1 and / or Figure 2. The program may be embodied in software stored on a tangible computer-readable storage medium, such as a CD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-ray disk, or memory associated with the processing unit 20, although the entire program and / or parts thereof may instead be executed by equipment other than the processing unit 20 and / or be embodied in firmware of dedicated hardware.

[0113] The program of Figure 4 begins when user input is received from one or more electronic devices such as those shown in Figure 1 (step a), block 202). The user defines the dosage form (block 202a), for example, via a graphical user interface of the illustrated example of Figure 3A. The dosage form can include at least one of: capsules, including hard capsules and soft capsules; chewing gums; creams; emulsions, including emulsion concentrates and microemulsions; foams; sprays; gels; sticks; granules; gummies; implants; ointments; pastes; pellets, including coated pellets; powders; solutions, including injection solutions; suppositories; suspensions, including suspension concentrates; sustained release formulations; tablets, including buccal tablets; chewable tablets; coated tablets; cleaning tablets; dishwashing tablets; effervescent tablets; lozenges; orodispersible tablets; vaginal tablets; and therapeutic patches.

[0114] The user also inputs the TPP (block 202b), for example, via a graphical user interface of the illustrated example of FIG. 3C. Examples of user-defined TPPs may include the following: User-defined TPPs applicable for all types of dosage forms, including but not limited to: amount and / or concentration of active ingredient; size, volume and / or weight of the dosage form; mechanical and / or rheological properties of the dosage form; release profile of the active ingredient; other application-related parameters; compatibility and stability; other manufacturing-related properties User-defined TPPs for solid oral dosage forms, including but not limited to: amount of AI per unit; size and / or weight of the dosage form; mechanical strength of the dosage form; desired release behavior of the dosage form; disintegration time of the dosage form; dissolution profile of the AI; compatibility of the active ingredient and excipients; probability of passing content uniformity criteria; flowability of the powder blend; tabletability of the powder blend; compatibility and stability of the active ingredient and excipients User-defined TPPs for liquid and semi-solid dosage forms, including but not limited to: concentration of the AI; volume of the dosage form; rheological behavior and / or viscosity of the dosage form; spreading and / or adhesion of the dosage form; dispersibility and / or volume fraction of the phases; hydrophilicity and / or lipophilicity; release behavior of the dosage form; melting point of the dosage form; dissolution profile of the AI; compatibility and stability of the active ingredient and excipients.

[0115] The user also inputs the key physicochemical properties of the AI ​​(block 202c), for example, via a graphical user interface of the illustrated example of FIG. 3B. Examples of key physicochemical properties of the AI ​​include at least one of the following: hydrophilicity and / or lipophilicity (e.g., partition coefficient); melting point; permeability across biological or artificial lipid membranes; solubility in water, solvents, co-solvents and / or bio-relevant media; miscibility with water, solvents, co-solvents and / or bio-relevant media; true density; viscosity; wettability; interfacial and / or surface tension; particle size distribution data; particle morphology, shape and / or aspect ratio; bulk and tapped density; flow properties (e.g., angle of repose or flow function coefficient); compressibility and moldability; hygroscopicity; moisture content (e.g., loss on drying); concentration of impurities; other chemical, physicochemical and / or physical properties; compatibility and stability information.

[0116] Next, the processing unit 30 of the exemplary apparatus 110 of FIG. 1 and / or FIG. 2 calculates the key parameters of the AI ​​related to the development of the dosage form based on the key physicochemical properties of the AI ​​[step b), block 204]. The key parameters of the AI ​​are calculated from the key physicochemical properties of the AI ​​by normalizing and scaling the data.

[0117] For example, the two most important parameters in the direct compression process are powder flowability and tabletability. Powder flowability can be characterized by measuring the angle of repose, the Hausner ratio or the flow rate through an orifice. Shear cell measurements, powder rheology and avalanche testing are more advanced tools to characterize powder flow. In contrast to flowability, powder tabletability is more difficult to evaluate. Measuring the resistance of tablets to crushing does not adequately describe the tabletability of a powder. And plotting the compressibility and compactibility profile of a material is time-consuming.

[0118] Models can be used to characterize the tabletability of the powder. To start the experiment, it is necessary to prepare tablets of the powder at, for example, five different compression pressure levels. Alternatively, if the powder is poorly compressible, it is conceivable to prepare tablets of the powder in combination with directly compressible excipients. In this case, the effect of the excipients is automatically subtracted by the processing unit 30. In the next step, the user measures the weight of the prepared tablets, their thickness and diameter, as well as their breaking load. The data can be entered via a command line interface or a graphical user interface. In the last step, the processing unit 30 calculates the porosity and tensile strength of the tablet. A modified version of the Gurnam equation can be used to model the tablet porosity as a function of the applied pressure [see GK Reynolds, JI Campbell, RJ Roberts, A compressibility based model for predicting the tensile strength of directly compressed pharmaceutical powder mixtures, International Journal of Pharmaceutics, 531 (2017) 215-22]:

[0119]

number

[0120] The Ryshkewitch-Duckworth equation can be used to describe the change in tensile strength with change in tablet density:

[0121]

number

[0122] Once all the data is entered, the processing unit 30 calculates the zero porosity compaction pressure, compressibility resistance, zero porosity tensile strength, and bonding capacity. These four values ​​are used to evaluate the tabletability of the powder.

[0123] To estimate the processability of a powder, different parameters can be considered. The parameters can be divided into categories such as particle size, powder density, flowability, and tabletability. All parameters can be scaled from 0 to 10, with 0 meaning "poor", 10 meaning "excellent", and 5 being the acceptable value for direct compression. The selected parameters and their limits are based on the manufacturing classification system [see M. Leane, K. Pitt, G. Reynolds, A proposal for a drug product Manufacturing Classification System (MCS) for oral solid dosage forms, Pharmaceutical Development and Technology, 20 (2015) 12-21]. Taking the angle of repose as an example, a value of 65° is normalized as 0, a value of 45° is normalized as 5, and a value of 25° is normalized as 10.

[0124] For example, 14 parameters (i.e., d10 value, d50 value, d90 value, distribution span, bulk density, tapped density, compressibility index, Hausner ratio, angle of repose, compaction pressure at 0.15 porosity, tensile strength at 0.15 porosity, tensile strength at 100 MPa compaction pressure, tensile strength at 150 MPa compaction pressure, and tensile strength at 250 MPa compaction pressure) may be used to evaluate the processability of an active ingredient. All parameters can be scaled from 0 to 10, with 0 meaning "poor," 10 meaning "excellent," and 5 being the acceptable value for direct compression.

[0125] The parameters can be classified into the following categories: processability (average values ​​of all parameters), particle size (average values ​​of d10, d50, d90 and distribution span), powder density (average values ​​of bulk and tapped density), powder flow (average values ​​of compressibility, Hausner ratio and angle of repose), and tabletability (average values ​​of compaction pressure at 0.15 porosity, tensile strength at 0.15 porosity, tensile strength at 100 MPa compaction pressure, tensile strength at 150 MPa compaction pressure and tensile strength at 250 MPa compaction pressure).

[0126] The radar chart can be used to quickly identify the strengths (values ​​≥ 5) and weaknesses (values ​​< 5) of an active ingredient. In general, it can be concluded that processability improves as the area enclosed by the curve increases. Direct compression is possible when processability, powder flowability and tabletability are ≥ 5; dry granulation (e.g. roller compaction, slugging, etc.) is possible when the tabletability parameters are ≥ 5; wet granulation (e.g. fluid bed or high shear granulation) is always possible. An example of the calculated key parameters of the AI ​​is shown in Figure 3D.

[0127] Optionally, the probability of passing the content uniformity test can be checked. The content uniformity of a pharmaceutical dosage form can be influenced by the particle size and particle size distribution of the active ingredient. Using the above described device, it is possible to set particle size limits for a given dose and eliminate particle size related content uniformity problems. A modified version of the Yalkowsky-Bolton equation can be used to calculate the relative standard deviation of a given particle size distribution [see BR Rohrs, GE Amidon, RH Meury, PJ Secreast, HM King, CJ Skoug, Particle size limits to meet USP content uniformity criteria for tablets and capsules, Journal of Pharmaceutical Sciences, 95 (2006) 1049-1059]. The theory assumes a homogeneous mixture and the particle size distribution is log-normal.

[0128] A diagram such as that shown in FIG. 3E can show the maximum volume median particle size (d50 value) predicted to pass the content uniformity test in stage I at a given confidence level (p-value) as a function of the distribution width (d90 / d50). This diagram can be used to estimate the particle size required to ensure that the content uniformity criteria are met. The diagram also demonstrates that reducing the width of the particle size distribution significantly increases the maximum acceptable d50 value. For example, by narrowing the distribution width (d90 / d50) from 4 to 2, the acceptable d50 value increases by about 4 times. Since larger particles affect content uniformity to a much greater extent, eliminating larger particles (e.g., by sieving) can significantly change the required particle size.

[0129] The processing unit 30 then predicts the key physicochemical properties of the API when combined with one or more excipients selected from the excipient database by applying mixing rules. The device then processes the data by normalizing and scaling the data and recalculates the key parameters of the API when combined with one or more excipients selected from the excipient database [step c), block 206]. The user can select one or more excipients via the exemplary graphical user interface of FIG. 3F. The recalculated key parameters are compared to the previously calculated key parameters to determine whether the insufficient properties of the API can be corrected with the excipients. For example, a radar chart as shown in FIG. 3G can be used to demonstrate the improvement.

[0130] In other words, the device predicts the processability of a powder blend (i.e., a combination of an active ingredient and a filler / binder). The user must enter the properties of the active ingredient (or select the active ingredient from a database), select an excipient or excipient blend from a list, and enter the weight fraction of the active ingredient.

[0131] For example, the properties of powder blends (i.e. particle size distribution, bulk density, tapped density, angle of repose, compressibility and moldability profiles) are estimated from single component data by applying rules of mixtures. To estimate the particle size distribution of a powder blend, the cumulative particle size distributions of the individual components are reconstructed from their d10, d50 and d90 values ​​(particles are assumed to be spherical and log-normally distributed). The cumulative particle size distribution of the powder blend is then derived from the volume-weighted arithmetic average of the individual curves.

[0132] The bulk density of the powder blend is calculated from the weighted arithmetic average of the individual values, with more weight given to components with smaller bulk densities. The tapped density of the powder blend is calculated from the weighted arithmetic average of the individual values, with more weight given to components with larger tapped densities. The angle of repose of the powder blend is calculated from the weighted arithmetic average of the individual values, with more weight given to components with larger angles of repose.

[0133] The compressibility profile of the powder blend is derived from the volume weighted arithmetic mean of the individual profiles; least squares fitting is performed to determine the compaction pressure at zero porosity and compressibility resistance. The compatibility profile of the powder blend is derived from the volume weighted geometric mean of the individual profiles; least squares fitting is performed to determine the tensile strength at zero porosity and bonding ability.

[0134] Next, the processing unit 30 of the exemplary apparatus 110 of FIG. 1 and / or FIG. 2 selects at least one promising excipient from the one or more selected excipients that can improve the important parameters of the AI ​​(step d), block 208) and suggests a manufacturing process based on the AI, the at least one selected promising excipient, and the dosage form (step e), block 210). A promising excipient is one that can better compensate for the insufficient properties of the API compared to other excipients. FIG. 3G shows an example of a promising excipient, namely Ludipress®, that can compensate for the deficient properties of the AI ​​so that the formulation is suitable for direct compression.

[0135] For example, the machine calculates the weight fraction of the active ingredient based on a given dose and tablet weight, and subtracts 7% from the tablet weight to accommodate disintegrants and lubricants. In the next step, the machine predicts the processability of all possible active ingredient-excipient combinations. The properties of the powder blends (i.e. particle size distribution, bulk density, tapped density, angle of repose, compressibility and compactability profiles) are estimated from the single component data by applying rules of mixtures.

[0136] Thus, 14 parameters (i.e., d10 value, d50 value, d90 value, distribution span, bulk density, tapped density, compressibility index, Hausner ratio, angle of repose, compaction pressure at 0.15 porosity, tensile strength at 0.15 porosity, tensile strength at 100 MPa compaction pressure, tensile strength at 150 MPa compaction pressure, and tensile strength at 250 MPa compaction pressure) may be used to evaluate the processability of the active ingredient. All parameters can be scaled from 0 to 10, with 0 meaning "poor", 10 meaning "excellent" and 5 being the acceptable value for direct compression.

[0137] The parameters can be classified into the categories of processability (average of all parameters), particle size (average of d10, d50, d90 values ​​and distribution span), powder density (average of bulk and tapped density), powder flow (average of compressibility, Hausner ratio and angle of repose) and tabletability (average of compaction pressure at 0.15 porosity, tensile strength at 0.15 porosity, tensile strength at 100 MPa compaction pressure, tensile strength at 150 MPa compaction pressure and tensile strength at 250 MPa compaction pressure).

[0138] Radar charts can also be used to quickly identify strengths (values ​​above 5) and weaknesses (values ​​below 5) of a powder blend. In general, it can be concluded that processability improves as the area enclosed by the curve increases. Direct compression is possible when processability, powder flow and tabletability are above 5; dry granulation (e.g. roller compaction, slugging, etc.) is possible when the tabletability parameters are above 5; wet granulation (e.g. fluid bed or high shear granulation) is always possible.

[0139] Once all combinations have been tested, the machine classifies the tested excipients and excipient blends according to their performance. For this purpose, a weighted average of all 14 parameters is calculated. More weight is given to tabletability and powder flowability, while particle size and powder density are less important. The machine then suggests an appropriate manufacturing process and selects the most suitable excipient or excipient blend.

[0140] Taking direct compression as an example, according to the manufacturing classification system, if a material meets certain requirements regarding particle size and shape, blend uniformity, powder flow, powder density, tabletability, and mechanical strength of the compact, direct compression is feasible.

[0141] For example, in the direct compression method, excipients and excipient blends with poor flowability and / or tabletability (values ​​less than 5) are screened out. All excipients and excipient blends are rated on a scale from zero ("not suitable") to five stars ("most suitable"). "Five stars" indicates the excipient or excipient blend with the best performance, "one star" indicates the excipient or excipient blend with the worst performance, and "zero stars" means that the excipient or excipient blend is not suitable for the direct compression process.

[0142] The device then calculates the starting formulation with the selected filler-binder combination. A superdisintegrant is added, the amount of which depends on the selected filler-binder combination. For example, if the filler-binder combination already contains a disintegrant (e.g., a ready-to-use excipient), the amount of superdisintegrant is reduced accordingly. If the tabletability of the powder blend is medium (a value below 6), a superdisintegrant with binding properties (e.g., Kollidon® CL-SF) is selected; otherwise, a normal superdisintegrant (e.g., Kollidon® CL-F) is added. Sodium stearyl fumarate is added as a lubricant, the amount of which depends on the selected filler-binder combination. In contrast to magnesium stearate, sodium stearyl fumarate does not cause excessive lubrication and is less incompatible with the active ingredient. Once the exact amounts of superdisintegrant and lubricant are determined, the amount of the filler-binder combination is recalculated.

[0143] The processing unit 30 of the example apparatus 110 of FIG. 1 and / or FIG. 2 then predicts product characteristics based on the proposed manufacturing process, the combination of the AI ​​with at least one selected promising excipient, and the dosage form [step f), block 212] and determines whether the predicted product characteristics comply with the user-defined TPP [step g), block 214].

[0144] If the predicted product characteristics are determined to fit the user-defined TPP (e.g., fall within a predefined range of the user-defined TPP), a suitable formulation may be identified based on the combination of the AI ​​and at least one selected promising excipient, a proposed manufacturing process, and a dosage form [step f), block 216]. An example of an identified formulation is shown in FIG. 3H. If at least one formulation is identified in step 216, the user may prepare the formulation in the laboratory and characterize the resulting product. If the experimental results (e.g., content uniformity, dissolution profile, tablet mechanical strength, etc.) fit the target profile, the development process ends. If not, the device may suggest optimizing the characteristics of the AI, adjusting the TPP, or selecting a different dosage form.

[0145] If it is determined that the predicted product characteristics do not conform to the user-defined TPP, the processing unit 30 of the exemplary apparatus 110 may suggest optimizing the characteristics of the AI ​​with at least one additional technical measure based on the difference between the predicted product characteristics and the user-defined TPP (block 218). The at least one additional technical measure includes at least one of grinding or micronization, addition of excipients, and processing. Alternatively or additionally, the processing unit 30 may suggest adjusting the TPP (block 220) or selecting a different dosage form (block 222) based on the difference between the predicted product characteristics and the user-defined TPP. The user then has the possibility to optimize the characteristics of the AI ​​(block 224), redefine the TPP (block 226), and / or reselect a dosage form (block 228). The processing unit 30 then executes steps b) to h) again until a suitable formulation with product characteristics that comply with the user-defined or user-redefined TPP is identified, and the program of FIG. 4 ends. The same procedure (blocks 218-228) can be applied when the experimental results do not match the target profile.

[0146] Optionally, the user has the possibility to print the formulation, download related information (e.g., quality, regulatory, and technical documents), and order product samples. Furthermore, the user has the possibility to provide feedback (e.g., regarding usability, information content, prescription results, etc.) that will be used to improve the user experience and the system itself.

[0147] The system may further have a list of examples including the final product, including its recipe, manufacturing technique, and properties. This list may be accessed by the user, for example, by searching for the active substance, dosage, and dosage form. The manufacturability and properties of these formulations have already been experimentally proven.

[0148] In another exemplary embodiment of the present invention, a computer program or computer program element is provided, characterized in that it is adapted to execute, on a suitable system, the method steps of the method according to one of the above-mentioned embodiments. The computer program element may therefore be stored in a computing unit which may be part of an embodiment of the present invention. This computing unit may be adapted to execute or direct the execution of the steps of the above-mentioned method. It may furthermore be adapted to operate the components of the above-mentioned device. The computing unit may be adapted to operate automatically and / or to execute the orders of a user. The computer program may be loaded into the operating memory of a data processor. The data processor may therefore be equipped to execute the method of the present invention.

[0149] This exemplary embodiment of the invention encompasses both computer programs that use the invention from the outset and computer programs that transform existing programs into programs that use the invention by modern means.

[0150] Moreover, the computer program element may provide all the steps required to carry out the procedures of the exemplary embodiments of the procedures described above.

[0151] According to a further exemplary embodiment of the present invention, a computer readable medium, such as a CD-ROM, is presented, having stored thereon a computer program element, which computer program element is described by the preceding section.

[0152] The computer program may be stored and / or distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems.

[0153] However, the computer program may also be presented on a network such as the World Wide Web and may be downloaded from such a network into the working memory of a data processor. According to a further exemplary embodiment of the invention, a medium for making a computer program element available for downloading is provided, the computer program element being configured to perform a method according to one of the aforementioned embodiments of the invention.

[0154] It should be noted that the embodiments of the present invention are described with reference to different subject matters. In particular, some embodiments are described with reference to method type claims, and other embodiments are described with reference to device type claims. However, those skilled in the art will understand from the above and following description that, unless otherwise indicated, any combination of features belonging to one type of subject matter, as well as any combination between features relating to different subject matters, is considered to be disclosed in this application. However, all features can be combined to provide a synergistic effect that is more than the simple sum of the features.

[0155] While the present invention has been illustrated and described in detail in the drawings and the foregoing description, such illustration and description should be considered as illustrative or exemplary and not restrictive. The present invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from studying the drawings, the disclosure, and the dependent claims. In the claims, the word "comprises" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain means are recited in mutually different dependent claims does not indicate that a combination of these means cannot be used to advantage. Any reference signs in the claims should not be interpreted as limiting the scope.

Claims

1. A computer-implemented method (200) for identifying suitable formulations for product development, comprising: a) via an input channel, Dosage form; Target Product Profile, TPP, containing minimum product requirements; - the main physicochemical properties of the active ingredient (AI); receiving (210) a user input defining b) calculating, by a processor, key parameters of the AI ​​relevant to the development of the dosage form based on key physicochemical properties of the AI ​​(220); c) predicting, by the processor, the key parameters of the AI ​​in combination with one or more excipients selected from an excipient database by applying mixing rules including predicting particle size using a cumulative distribution function (230); d) identifying, by said processor, at least one promising excipient from said one or more selected excipients that can improve said key parameters of said AI (240); e) suggesting, by the processor, the AI-based manufacturing process, the at least one selected potential excipient, and the dosage form (250); f) predicting, by the processor, product characteristics based on the proposed manufacturing process, the combination of the AI ​​with the at least one selected promising excipient, and the dosage form (260); g) determining, by the processor, whether the predicted product characteristics comply with TPPs defined by the user (270); h) if the predicted product characteristics are determined to comply with the user-defined TPPs, identifying, by the processor (280), a suitable formulation based on the combination of the AI ​​and the at least one selected potential excipient, the proposed manufacturing process, and the dosage form; having The method further comprises: If the predicted product characteristics are determined to be incompatible with the TPP defined by the user, or if the experimental results obtained after preparation and characterization of the identified suitable formulation are determined to be incompatible with the TPP defined by the user, - suggesting at least one additional technical measure for optimizing the key parameters of the AI ​​based on the difference between the predicted product characteristics and the TPP defined by the user or the difference between the experimental results and the TPP defined by the user; Run The at least one additional technical means is - Grinding or pulverization; Addition and processing of excipients; At least one of: method.

2. The method further comprises: If the predicted product characteristics are determined to be incompatible with the TPP defined by the user, or if the experimental results obtained after preparation and characterization of the identified suitable formulation are determined to be incompatible with the TPP defined by the user, - suggesting adjustments to the user-defined TPP based on differences between the predicted product characteristics and the user-defined TPP or between the experimental results and the user-defined TPP; - suggesting the selection of a different dosage form based on the difference between the predicted product characteristics and the user-defined TPP or the difference between the experimental results and the user-defined TPP; Executing at least one of the following: The method of claim 1.

3. The method further comprises: if the predicted product characteristics do not conform to the TPP defined by the user, or if it is determined that experimental results obtained after preparation and characterization of the identified suitable formulation do not conform to the TPP defined by the user; - receiving further user input related to a different dosage form, a user-redefined TPP, and / or redetermining key physicochemical properties of said AI; - carrying out steps b) to h) until a suitable formulation is identified having product characteristics that comply with the user-defined or redefined TPPs; Repeatedly executing a sequence including The method of claim 2.

4. The product development is -Development of cleaning products; - Cosmetics development; -Development of dietary supplements; - Development of pharmaceutical formulations; - Development of fungicide formulations; -Development of herbicide formulations; -Development of pesticide formulations; -Development of cleaning agents; At least one of:

4. The method according to any one of claims 1 to 3.

5. The dosage form comprises: Capsules including hard capsules and soft capsules, chewing gums, creams, emulsions including emulsion concentrates and microemulsions, foams, sprays, gels, sticks, granules, gummies, implants, ointments, pastes, pellets including coated pellets, powders, solutions including injection solutions, suppositories, suspensions including suspension concentrates, sustained release preparations, tablets including buccal tablets, chewable tablets, coated tablets, cleaning tablets, dishwashing tablets, effervescent tablets, lozenges, orodispersible tablets, vaginal tablets, therapeutic patches, At least one of:

5. The method according to any one of claims 1 to 4.

6. The user-defined TPP is the amount and / or concentration of said active ingredient; - the size, volume and / or weight of said dosage form; the mechanical and / or rheological properties of said dosage form; - the release profile of said active ingredient; Other application related parameters; - compatibility and stability; - other production related properties; At least one of:

6. The method according to any one of claims 1 to 5.

7. The user-defined TPP is Amount of AI per unit; - the size and / or weight of said dosage form; - the mechanical strength of the dosage form; - the desired release behavior of said dosage form; - the disintegration time of said dosage form; - the dissolution profile of said AI; - compatibility of the active ingredient with the excipients; Probability of passing content uniformity criteria; - flowability of the powder blend; Tabletability of powder blends; - compatibility and stability of active ingredients and excipients; At least one of: The method of claim 6.

8. The user-defined TPP is Concentration of AI; - the volume of said dosage form; - Theological behavior and / or viscosity of the dosage form; - spreading and / or adhesion of the dosage form; - dispersibility and / or volume fraction of the phases; - hydrophilic and / or lipophilic; - the release behaviour of said dosage form; the melting point of said dosage form; - the dissolution profile of said AI; - compatibility and stability of active ingredients and excipients; At least one of: The method of claim 6.

9. The main physicochemical properties of the AI ​​are: - hydrophilicity and / or lipophilicity (e.g. partition coefficient); Melting point; - permeability of biological or artificial lipid membranes; - solubility in water, solvents, co-solvents and / or biorelevant media; - miscibility with water, solvents, co-solvents and / or biorelevant media; ・True density; ·viscosity; ・Wettability; - interfacial and / or surface tension; Particle size distribution data; Particle morphology, shape and / or aspect ratio; - Bulk and tapped density; ・Liquidity; - Compressibility and moldability; ・Hygroscopicity; ・Moisture content; ・Impurity concentration; - other chemical, physicochemical and / or physical properties; - compatibility and stability information; At least one of:

9. The method according to any one of claims 1 to 8.

10. the user-defined TPP includes a dose of an AI per unit and a maximum weight of the dosage form; Step c) further comprises: - calculating the weight fraction of the AI ​​and the one or more excipients selected from the excipient database based on the dose of the AI ​​per unit and the maximum weight of the dosage form; - predicting the properties of the combination of said AI with said one or more excipients; having Step d) further comprises selecting at least one potential excipient from said one or more excipients if the properties of the corresponding mixture satisfy predetermined criteria.

10. The method according to any one of claims 1 to 9.

11. The method of claim 1 , wherein the dosage form comprises a pharmaceutical dosage form.

12. An apparatus (110) for identifying a suitable formulation for product development, comprising: an input unit (10); A processing unit (20), a) via an input channel, Dosage form; Target Product Profile, TPP, containing minimum product requirements; - the main physicochemical properties of the active ingredient (AI); receiving user input defining b) calculating the main parameters of said AI relevant for the development of said dosage form based on the main physicochemical properties of said AI; c) recalculating the key parameters of the AI ​​when combined with one or more excipients selected from an excipient database; d) selecting at least one promising excipient from said one or more selected excipients capable of improving said key parameters of said AI; e) proposing said AI-based manufacturing process, said at least one selected potential excipient, and said dosage form; f) predicting product characteristics based on the proposed manufacturing process, the combination of the AI ​​with the at least one selected potential excipient, and the dosage form; g) determining whether the predicted product characteristics comply with the user-defined TPPs; h) identifying a suitable formulation based on the combination of the AI ​​and the at least one selected potential excipient, the proposed manufacturing process, and the dosage form if the predicted product characteristics are determined to comply with the user-defined TPPs; a processing unit configured to perform the Equipped with The processing unit further comprises: If the predicted product characteristics are determined to be incompatible with the TPP defined by the user, or if the experimental results obtained after preparation and characterization of the identified suitable formulation are determined to be incompatible with the TPP defined by the user, - suggesting at least one additional technical measure for optimizing the key parameters of the AI ​​based on the difference between the predicted product characteristics and the TPP defined by the user or the difference between the experimental results and the TPP defined by the user; Run The at least one additional technical means is - Grinding or pulverization; Addition and processing of excipients; At least one of: Device.

13. A system (100) for identifying a suitable formulation for product development, comprising: - device (110) according to claim 12; A web server (140) configured to interact with users via web pages and / or application programs provided by the web server; Equipped with the device is configured to provide a graphical user interface (GUI) to a user via the web page and / or the application program; system.

14. A computer program element comprising a set of instructions, which, when executed on a processor of an apparatus according to claim 12, causes said apparatus to carry out a method according to any one of claims 1 to 11.

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