Method for measuring drug resistance

Abstract

The present invention concerns methods for measuring drug resistance by correlating genotypic information with phenotypic profiles. In one embodiment, a method for interpreting genotypic information is described wherein a genetic code is generated from a patient sample, a list of mutations known or suspect to play a role in the development of resistance to one or more drugs is obtained from the generated genetic code, a genotype database is interrogated for previous samples with similar mutations relating to said one or more drugs, a phenotype for said samples is located in a phenotype database, the mean change in inhibition is determined based on all the examples located in said phenotype database, and a phenotype is determined for the patients sample. Furthermore, methods are provided for predicting a phenotype from a biological sample and for predicting drug or therapy resistance of a patient, a pathogen or a malignant cell. Also methods and systems are provided for designing, optimizing and assessing the efficiency of a therapeutic regimen based upon the genotype of the disease affecting the patient.

Description

[0001] The present invention concerns methods and systems for predicting the resistance of a disease to a therapeutic agent. More specifically, the invention provides methods for predicting drug resistance by correlating genotypic information with phenotypic profiles. The invention further relates to methods and systems for designing, optimizing and assessing he efficiency of a therapeutic regimen based upon the genotype of the disease affecting the patient.BACKGROUND TO THE INVENTION[0002] Techniques to determine the resistance of a pathogen or malignant cell to a therapeutic agent are becoming increasingly important. For example, despite the great advantages of existing treatments against viral infections such as HIV infection, cancer and bacterial infections, many patients experience treatment failure or reduced efficacy over time. In many instances this is due to the pathogen, malignant cell, bacteria, virus or other disease state mutating and / or developing a resistance to the t...

AI Technical Summary

Benefits of technology

[0094] Reference or wild type sequences for use in the invention in the field of specific diseases, infections or diseases caused by specific pathogens can be easily obtained from publicly available databases. For example, the influence of mutations on the etiology of cancer can be exemplified by the mutations influencing the effect of the tumor suppressor gene such as p53, TGF-beta, NF-1, WT-1, and Rb. Also, mutations present in oncogenes such as Ras, c-myc, c-raf, neu, and IL-2, and repair genes, e.g., methylguanosyl and methyltransferase can cause changes in the phenotype and / or drug effect.

[0134] According to a preferred embodiment, the present invention provides a thorough and reliable interpretation of genotypic information by interrogating the genotype part of a relational genotype / phenotype database for identical or similar patterns of mutations to that of the patient sample under study. Once the matches are found, the corresponding phenotypes are accessed and the phenotypic information, the changes in IC.sub.50 to the various drugs, is pooled and averaged to produce a phenotypic profile. This profile, in one embodiment of the invention, may be based on data from hundred or thousands of real phenotypes with the same patterns of mutations. In another embodiment, the RT-PR region of the HIV-1 genome of a patient sample is sequenced and the sequence is used in the methods of the invention to interpret the genotype information. The virtual phenotype may then be used to design a therapy, which may be one or more drugs. In a further embodiment, proprietary software may be used to interpret the genotype information according to the methods of the invention.

[0136] In another embodiment, the number and the combinations of mutations used to construct a mutation pattern would be updated on a regular basis. This may be done in order to incorporate newly identified mutations or combinations which may improve the performance of the system. In one embodiment, a phenotype may be calculated from at least one mutation used to construct a mutation pattern, however, from a statistical perspective a more accurate phenotype may result from a greater number of mutations.

Problems solved by technology

For example, despite the great advantages of existing treatments against viral infections such as HIV infection, cancer and bacterial infections, many patients experience treatment failure or reduced efficacy over time.

Initially, and due to a lack of alternative drugs, these agents were administered alone, as monotherapy.

Though a temporary antiviral effect was observed, all the compounds lost their effectiveness over time.

Since then, research has demonstrated that one of the main reasons behind treatment failure for all the antiviral drugs is the development of resistance of the virus to the drug.

These inhibitors showed greater potency than the nucleosides, but again were prone to resistance when used alone.

In recent years, however, it has become clear that even patients being treated with triple therapy including a protease inhibitor often eventually experience treatment failure.

Data suggests that up to one half of patients on combination therapy do not achieve or do not maintain suppression of virus replication.

In some cases, it may be that even state-of-the-art triple therapy is insufficient to halt viral replication.

Another factor contributing to the difficulty to maintain suppression of virus replication has been the sheer burden of taking up to 20 pills each day, at set times, with or without food, day after day.

It is simply unrealistic to expect people to adhere to such stringent and demanding regimens indefinitely.

But if patients do not adhere, the price can be high.

In addition, not all HIV-1 infections originate with a wild type, drug sensitive strain from which drug resistance will emerge.

However, it is not easy for the physician to establish the optimal combination for an individual.

Viral load, however, provides no information or guidance regarding which drugs should be used.

Resistance emergence is highly predictive of treatment failure.

However, the interactions between different viral mutations related to different inhibitors is so complex that selecting the optimal treatment combination with only a treatment history to go on is far from ideal.

It is clear that although there are many drugs available for use in combination therapy, the choices can quickly be exhausted and the patient can rapidly experience clinical progression or deterioration if the wrong treatment decisions are made.

A drawback of this method is that it does not detect NSI strains.

A problem with this technique is that virus culture from PBMCs is very slow and labor-intensive.

In addition, it lacks the precision of other techniques and because it relies on primary human cells for virus growth, assay automation and high throughput is virtually impossible.

Point mutation assays can only provide a small select part of the resistance picture.

However, at present, it remains difficult to interpret the results of a genotypic test to provide meaningful conclusions about therapeutic agent resistance.

Disadvantages of phenotyping are that it is complex, lengthy to perform, (usually 4 weeks) and, therefore, more expensive than genotyping.

Thus, phenotyping is not a practical way of designing patient therapy.

Consequently, control of viral replication is lost and several of the 15 drugs available have been `used up`.

Although genotyping tests can be performed more rapidly, a problem with genotyping is that there are now over 100 individual mutations with evidence of an effect on susceptibility to HIV-1 drugs and new ones are constantly being discovered, in parallel with the development of new drugs and treatment strategies.

The relationship between these point mutations, deletions and insertions and the actual susceptibility of the virus to drug therapy is extremely complex and interactive.

Consequently, the interpretation of genotypic data is both highly complex and critically important.

As more drugs become available and as more mutations are involved in the development of resistance, the `manual` or rules-based interpretation of raw genotype data is rapidly becoming impossible due to an increase in complexity.

Therefore, the main challenge involved with genotyping is improving the interpretation of the results.

These diseases states are subject to complex and continuously varying therapy regimens and therefore the patient under treatment needs to undergo frequent therapy monitoring in order to follow the drug effect or in order to optimize or select the optimal patient management.

The term "malignant cell" relates to a cell showing an abnormal structure or behavior in the organism containing it, resulting in a severe disease state.

By taking less mutation (or hot-spots) one will still be able to calculate the phenotype, however, from a statistical perspective the performance of the system will lower.

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