Chapter 7: Pharmacogenomics

<div><p>There is great variation in drug-response phenotypes, and a “one size fits all” paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.</p> </div

Drug discovery.

<p>Pharmacogenomics can be used at multiple steps along the drug discovery pipeline to minimize costs, as well as increase throughput and safety. First, association and expression methods (as well as pathway analysis) can be used to identify potential gene targets for a given disease. Cheminformatics can then be used to narrow the number of targets to be tested biochemically, as well as identifying potential polypharmacological factors that could contribute to adverse events. After initial trials (including animal models and Phase I trials), pharmacogenomics can identify variants that may potentially affect dosing and efficacy. This information can then be used in designing a larger Phase III clinical trial, excluding “non-responding” and targeting the drug towards those more likely to respond favorably.</p

Applying pharmacogenomics in the clinic.

<p>A proposed clinical workflow including pharmacogenomic information. A physician considers the patient's current presentation and past history when coming up with a working diagnosis and based on his or her clinical judgment, decides what drugs the patient may need. For example, if the physician wanted to add clopidogrel to the patient's regimen, the physician would input it into the electronic medical record (EMR). The EMR would interrogate the genome and present a message such as “clopidogrel sensitivity: POOR METABOLIZER, REDUCED ANTI-PLATELET EFFECT - gene: CYP2C19 - gene result *2/*2.” Based on this recommendation, the physician may adjust the dose accordingly or choose another drug. In this case, the physician will likely increase the dose of clopidogrel in order to achieve therapeutic effect. Reprinted with permission from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002817#pcbi.1002817-Pulley1" target="_blank">[65]</a>.</p

Methotrexate binds to the folate-binding region of DHFR.

<p>(A) Structural similarity between methotrexate and dihydrofolate. (B) Methotrexate (green) and dihydrofolate (blue) fit into the same binding pocket of DHFR. (C) The conformation of dihydrofolate bound to the reference version of the receptor. (D–E) Two possible conformations of dihydrofolate bound to the F31R/Q35E variants of the receptor. These variants have decreased affinity to methotrexate, relative to dihydrofolate. Reprinted with permission from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002817#pcbi.1002817-Volpato1" target="_blank">[8]</a>.</p

Cheminformatics methods.

<p>New associations discovered by cheminformatics methods. The Similarity Ensemble Approach (SEA) uses ligand similarity methods to discover potential new associations between drugs and targets. Reprinted with permission from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002817#pcbi.1002817-Keiser2" target="_blank">[33]</a>.</p

Examples of pharmacogenomics used in this chapter. Additional examples can be found at PharmGKB.

<p>Examples of pharmacogenomics used in this chapter. Additional examples can be found at PharmGKB.</p

Association methods.

<p>(A) An association study with cases and controls. Millions of genetic loci are probed to ascertain “association,” or separation between genotypes in cases and controls. (B) Each SNP is tested independently using a 2×2 contingency table and a χ² test or Fisher's exact test. (C) Each SNP is assessed for “genome-wide” significance, after Bonferroni correction. Reprinted from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002817#pcbi.1002817-Takeuchi1" target="_blank">[64]</a>.</p

Drug repurposing.

<p>Docking methods suggest binding site similarity between COMT (green) and InhA (blue). The overlap between the predicted locations of their cofactors (purple and orange, respectively) and ligands (red and yellow, respectively) suggest potential similarity in their functions. Thus, the same drug that has been used to inhibit COMT (entacapone) was predicted to inhibit the M. tuberculosis protein InhA for potential treatment of tuberculosis. Reprinted from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002817#pcbi.1002817-Kinnings1" target="_blank">[36]</a>.</p

Student perceptions of knowledge about genetics and personal genome testing.

a<p>The number (and percentage) of subjects who agreed or strongly agreed with each statement is reported.</p>b<p>Wilcoxon signed-rank test comparing pre- to post-course responses.</p>c<p>Mann-Whitney <i>U</i>-test comparing post-course responses between genotyped and non-genotyped groups.</p

Student reflection on genotyping offer and experience.

a<p>The number (and percentage) of subjects who agreed or strongly agreed with each statement is reported.</p>b<p>Mann-Whitney <i>U</i>-test comparing post-course responses between genotyped and non-genotyped groups.</p