Improved drug therapy: triangulating phenomics with genomics and metabolomics

Abstract

Embracing the complexity of biological systems has a greater likelihood to improve prediction of clinical drug response. Here we discuss limitations of a singular focus on genomics, epigenomics, proteomics, transcriptomics, metabolomics, or phenomics-highlighting the strengths and weaknesses of each individual technique. In contrast, 'systems biology' is proposed to allow clinicians and scientists to extract benefits from each technique, while limiting associated weaknesses by supplementing with other techniques when appropriate. Perfect predictive modeling is not possible, whereas modeling of intertwined phenomic responses using genomic stratification with metabolomic modifications may greatly improve predictive values for drug therapy. We thus propose a novel-integrated approach to personalized medicine that begins with phenomic data, is stratified by genomics, and ultimately refined by metabolomic pathway data. Whereas perfect prediction of efficacy and safety of drug therapy is not possible, improvements can be achieved by embracing the complexity of the biological system. Starting with phenomics, the combination of linking metabolomics to identify common biologic pathways and then stratifying by genomic architecture, might increase predictive values. This systems biology approach has the potential, in specific subsets of patients, to avoid drug therapy that will be either ineffective or unsafe.

Figure 1

Illustration of an integrated systems biology approach to improve drug therapy. Using phenomics to fully characterize clinical traits associated with drug therapy. When combined with metabolomics, common biological pathways can be identified, providing insight into mechanisms of efficacy and safety. When phenomic data associated with genomics data are also combined, pleiotropic associations can be further identified and contribute to our understanding of underlying biological pathways. Other techniques, such as RNA-seq, can be integrated to add depth to the pathway data and supplement our understanding of genomic expression.

Figure 2

A clinical example of the proposed integrated method for predicting drug response. (1) Patients are given 10 mg of hydrocodone. (2) Clinical phenotypes are captured fully and completely. These may include (among others) development of ADRs, chronicity of treatment, ethnic differences, and demographic factors. (3) Association studies may contribute to characterization of the clinical phenotypes (e.g. RNA-sequencing may help distinguish chronicity of treatment). (4) The drug response is categorized into phenotypically pertinent groups. (5) Relevant biological pathways are identified and linked by individual metabolomic markers. (6) Stratification of drug response is refined by accounting for biological pathway polymorphisms and controlled for phenotypic variables captured in #2 above. (7) The final stepwise model is built, allowing for a high, although not perfect, receiver operating characteristic (ROC).