Multigenic Control of Drug Response and Regulatory Decision-Making in Pharmacogenomics: The Need for an Upper-Bound Estimate of Genetic Contributions
Bibliographic record
Abstract
Nature or nurture? To what extent genetics play a role in drug efficacy and safety? These questions are not new. They are however gaining increasing prominence with the implementation of pharmacogenomics in various facets of medicine ranging from therapeutics, drug development and regulatory science to research funding decisions. For predisposition to common complex diseases, twin and family studies have been the mainstay for estimating genetic components of the attendant risk. On the other hand, the rapid pace of drug development in the pharmaceutical industry and the need for faster regulatory decisions call for an approach of higher throughput to identify the compounds for which heritability is likely to play a significant role in their pharmacokinetics and/or pharmacodynamics. A second predicament related to multifactorial nature of drug effects is that one typically observes a considerable overlap in the distribution of drug response phenotypes among subpopulations identified by each pharmacogenomic biomarker. This is in sharp contrast to monogenic pharmacological traits wherein it is feasible to partition the patient populations into discrete subgroups by analysis of a single gene. Hence, as pharmacogenomic investigations progress from monogenic to increasingly multigenic or multifactorial drug response phenotypes, the regulatory decision-makers are faced with a dilemma: How can a reviewer or a clinician determine if a given separation of a drug response profile by a pharmacogenomic biomarker is worthwhile for clinical implementation? The present manuscript makes an attempt to address these broad and emerging issues in pharmacogenomics and regulatory science. We propose that a comparison of inter- versus intra-subject variability in drug response under minimal environmental exposure may provide an upperbound estimate of heritability of drug efficacy and safety. It is also argued that seemingly modest changes in population averages may underestimate the dramatic impact of a genetic biomarker at the tails of a population. To this end, a conceptual framework for graded risk assessment among subpopulations with overlapping quantitative phenotypes is presented. We conclude with a broader discussion of the evolution of genetic biomarkers from monogenic to multigenic traits in pharmacology, the associated ethical, social and therapeutic policy corollaries and the challenges lying ahead. Keywords: pharmacogenomics, biomarkers, genetic components, twin study, drug regulation, repeated drug administration study, rda, risk assessment, bioethics, therapeutic policy
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.124 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".