Benefits of Pharmacogenomics in Drug Development—Earlier Launch of Drugs and Less Adverse Events

Genophenotypic Factors and Pharmacogenomics in Adverse Drug Reactions

Adverse drug reactions (ADRs) rank as one of the top 10 leading causes of death and illness in developed countries. ADRs show differential features depending upon genotype, age, sex, race, pathology, drug category, route of administration, and drug–drug interactions. Pharmacogenomics (PGx) provides the physician effective clues for optimizing drug efficacy and safety in major problems of health such as cardiovascular disease and associated disorders, cancer and brain disorders. Important aspects to be considered are also the impact of immunopharmacogenomics in cutaneous ADRs as well as the influence of genomic factors associated with COVID-19 and vaccination strategies. Major limitations for the routine use of PGx procedures for ADRs prevention are the lack of education and training in physicians and pharmacists, poor characterization of drug-related PGx, unspecific biomarkers of drug efficacy and toxicity, cost-effectiveness, administrative problems in health organizations, and insufficient regulation for the generalized use of PGx in the clinical setting. The implementation of PGx requires: (i) education of physicians and all other parties involved in the use and benefits of PGx; (ii) prospective studies to demonstrate the benefits of PGx genotyping; (iii) standardization of PGx procedures and development of clinical guidelines; (iv) NGS and microarrays to cover genes with high PGx potential; and (v) new regulations for PGx-related drug development and PGx drug labelling.

Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models

Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The development of Big Data and deep learning technologies significantly improve the processing of unstructured data and unleash the great potential of QSAR. Here we discuss the integration of wet experiments (which provide experimental data and reliable verification), molecular dynamics simulation (which provides mechanistic interpretation at the atomic/molecular levels), and machine learning (including deep learning) techniques to improve QSAR models. We first review the history of traditional QSAR and point out its problems. We then propose a better QSAR model characterized by a new iterative framework to integrate machine learning with disparate data input. Finally, we discuss the application of QSAR and machine learning to many practical research fields, including drug development and clinical trials.

Pharmacogenomics in early-phase clinical development

Pharmacogenomics (PGx) offers the promise of utilizing genetic fingerprints to predict individual responses to drugs in terms of safety, efficacy and pharmacokinetics. Early-phase clinical trial PGx applications can identify human genome variations that are meaningful to study design, selection of participants, allocation of resources and clinical research ethics. Results can inform later-phase study design and pipeline developmental decisions. Nevertheless, our review of the clinicaltrials.gov database demonstrates that PGx is rarely used by drug developers. Of the total 323 trials that included PGx as an outcome, 80% have been conducted by academic institutions after initial regulatory approval. Barriers for the application of PGx are discussed. We propose a framework for the role of PGx in early-phase drug development and recommend PGx be universally considered in study design, result interpretation and hypothesis generation for later-phase studies, but PGx results from underpowered studies should not be used by themselves to terminate drug-development programs.

Pharmacogenomic technologies: a necessary "luxury" for better global public health?

BACKGROUND

Pharmacogenomic technologies aim to redirect drug development to increase safety and efficacy of individual care. There is much hope that their implementation in the drug development process will help respond to population health needs, particularly in developing countries. However, there is also fear that novel pharmacogenomic drugs will remain too costly, be designed for the needs of the wealthy nations, and so constitute an unnecessary "luxury" for most populations. In this paper, we analyse the promise that pharmacogenomic technologies hold for improving global public health and identify strategies and challenges associated with their implementation.

DISCUSSION

This paper evaluates the capacity of pharmacogenomic technologies to meet six criteria described by the University of Toronto Joint Centre for Bioethics group: 1) impact of the technology, 2) technology appropriateness, 3) capacity to address local burdens, 4) feasibility to be implemented in reasonable time, 5) capacity to reduce the knowledge gap, and 6) capacity for indirect benefits. We argue that the implementation of pharmacogenomic technologies in the drug development process can positively impact population health. However, this positive impact depends on how and for which purposes the technologies are used. We discuss the potential of these technologies to stimulate drug discovery in the case of rare (orphan diseases) or neglected diseases, but also to reduce acute adverse drug reactions in infectious disease treatment and prevention, which promises to improve global public health.

CONCLUSIONS

The implementation of pharmacogenomic technologies may lead to the development of drugs that appear to be a "luxury" for populations in need of numerous interventions that are known to have a demonstrable impact on population health (e.g., secure access to potable water, reduction of social inequities, health education). However, our analysis shows that pharmacogenomic technologies do have the potential to redirect drug development and distribution so as to improve the health of vulnerable populations. Strategies should thus be developed to better direct their implementation towards meeting the needs and responding to the realities of populations of the developing world (i.e., social, cultural and political acceptability, and local health burdens), making pharmacogenomic technologies a necessary "luxury" for global public health.

Bioinformatics challenges for personalized medicine

MOTIVATION

Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.

RESULTS

This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.

CONTACT

russ.altman@stanford.edu