Plenge Lab
Date posted: February 8, 2013 | Author: | No Comments »

Categories: Drug Discovery Precision Medicine

Convergence Ahead Road SignGenetics can guide the first phase of drug development (identifying drug targets, see here ) as well as late phase clinical trials (e.g., patient segmentation for response/non-responder status, see here ). But is there a convergence between the two areas, or pharmaco-convergence (a term I just made up!)? And are there advantages to a program anchored at both ends in human genetics?


Consider the following two hypothetical examples.

(1) Human genetics identifies loss-of-function (LOF) mutations that protect from disease. The same LOF mutation is associated with an intermediate biomarker, but is not associated with other phenotypes that might be considered adverse drug events. A drug is developed that mimics the effect of the mutation; that is, a drug is developed that inhibits the protein product of the gene. In early mechanistic studies, the drug is shown to influence the intermediate biomarker in a way that is consistent to that predicted by the LOF-protective mutations. Further, because functional studies of the LOF-protective mutations provide insight into relevant biological pathways in humans (e.g., a gene expression signature that correlates with mutation carrier status), additional information is known about genomic signatures of those who carry the LOF-protective mutations (which mimics drug exposure) compared to those who do not carry the LOF-protective mutations (which mimics those who are not exposed to drug). Now, a large-scale phase II clinical trial is ready to be launched.

The CEO of a company asks: How should the clinical trial be designed?

(2) A drug is approved for use in a disease. At the time of initial approval, there is no genomic or clinical biomarkers of efficacy or toxicity. Subsequent pharmacogenomic studies demonstrate that there are inherited DNA variants that influence the drug therapeutic window, such that patients who carry a specific genotype are much less likely to respond to the drug, regardless of the drug dose.

Now, the CEO of the company that developed drug asks a basic question: If such information were known at the time a large-scale phase II clinical trial was being designed, would the information have influenced the design of the clinical trial?

Potential answers to the CEO for question #1 – (a) The genomic signature that is associated with LOF mutation carrier status should be measured as a biomarker to predict efficacy/toxicity; if predictive, the biomarker should be used in subsequent trials to stratify enrollment. (b) The LOF-protective mutations themselves should be tested for association with efficacy/toxicity; if predictive, the mutations should be used in subsequent trials to stratify enrollment.

A potential answer to question #2 – The DNA variants should have been used to stratify enrollment – those who are more likely to respond should be preferentially enrolled in the trial.

In other words, both scenarios converge on a central theme: a genomic signature — whether expression-based or genotype-based — should be used to stratify enrollment into the clinical trial in order to maximize efficacy and minimize toxicity.

Now consider a third, overlapping scenario:

(3) Drug X is already approved for a disease indication; a biomarker is developed that predicts either non-response to that drug, or predicts an adverse event to that drug. At the same time, a different drug, Drug Y, is developed based on human genetics (e.g., LOF mutations that protect from disease), and a phase II clinical trial is being designed.

The CEO now asks: How should this information be used in designing the phase II clinical trial for Drug Y?

The response is similar, except for one important twist: the biomarker that predicts non-response to the approved Drug X should be used to enroll patients for the new Drug Y.

These three scenarios highlight what I think is an important principle: an effective drug discovery program should converge from both ends of the pipeline onto the early clinical trials that ultimately determine whether a drug is safe and effective in humans. I argue that a drug discovery program that is anchored in human genetics has the key advantage of being able to link fundamental biological discoveries made at the time of target validation with fundamental discoveries made for approved drugs used for the same indication. This is quite powerful, as it merges technologies and insights from the entire drug discovery program to both develop new drugs and optimize use of drugs that are already approved.

In the Plenge Lab, we are striving to converge both areas. We search for LOF mutations that protect from rheumatoid arthritis (RA), with the goal of launching a biochemical screen of small molecule compounds that mimic the effect of the human mutations. We also study the consequences of human genetic mutations, as we have done with the CD40 risk variant. In addition, we study genomic predictors of response to anti-TNF therapy in RA. Over time, we anticipate that the two areas will converge to improve drug development in RA.

Pharmacogenetics in the evaluation of new drugs: a multiregional regulatory perspective

In a February 2013 perspective published in Nature Reviews Drug Discovery, a diverse group published a an opinion piece about the use of pharmacogenomics (PGx) during the development of novel drugs (download PDF here). A primary aim of the manuscript is: “to describe the guidelines from the EMA, the FDA and the PMDA, focusing on critical issues for the use of pharmacogenetics during drug development related to drug PK parameters“. [abbreviations: European Medicines Agency (EMA); US Food and Drug Administration (FDA); and Pharmaceuticals and Medical Devices Agency (PMDA)]

The article places PGx genes into three categories (see Table 2 of the paper): (1) genes relevant to the drug’s absorption, distribution, metabolism and excretion (ADME) – or PK genes; (2), genes that encode drug targets – or PD genes; and (3) genes that influence disease susceptibility or progression. They focus on the first category of genes (PK genes), but with an interesting twist: they provide insight into how regulatory agencies globally have been developing guidance for drug developers, with the goal of ensuring satisfactory efficacy and lowering the incidence of adverse drug events (ADE’s) associated with novel drugs.

They provide recommendations on the conduct of PGx studies at different phases of drug development to optimize drug PK parameters. The issues discussed include: (1) situations in which PGx studies are required or recommended; (2) the banking of DNA from trial participants, which can help ensure that unknown genetic variants or important metabolic pathways can be retrospectively identified and their clinical effects tested with sufficient power; and (3) the translation of knowledge into drug labelling (for example, in recommendations for dosing adjustments).

This manuscript provides a practical way in which PGx fields can converge – with regulatory agencies paying attention to developments in the field.

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