Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine

Abstract

The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of clinical medicine and pharmacology. Principles of systems pharmacology can be applied to drug design and, ultimately, testing in human clinical trials. Rather than focusing exclusively on single drug targets, systems pharmacology examines the holistic response of a phenotype-dependent pathway or pathways to drug perturbation. Knowledge of individual pharmacogenetic profiles further modulates the responses to these drug perturbations, moving the field toward more individualized ('personalized') drug development. The speed with which the information required to assess these system responses and their genomic underpinnings is changing and the importance of identifying the optimal drug or drug combinations for maximal benefit and minimal risk require that clinical trial design strategies be adaptable. In this paper, we review the tenets of adaptive clinical trial design as they may apply to an era of expanding knowledge of systems pharmacology and pharmacogenomics, and clinical trail design in network medicine.

Figure 1. Schematic diagram illustrating the stages and time course of drug development

Once pre-clinical testing is complete, the first-in-human (FIH) and proof-of-concept (POC) studies are performed. These are critical elements of the exploratory phase of drug development, which is followed by the confirmatory phase, characterized by large Phase III, registration-pathway trials. As emphasized at the bottom of the figure, adaptive design has its greatest utility in guiding the exploratory phase of development. (Modified from Alexander JC, Salazar DE. Modern Drug Discovery and Development. In: Robertson D, Williams GH, eds. Clinical and Translational Science: Principles of Human Research. London: Academic Press; 2009:361-380.)

Figure 2. The ratio of non-coding to protein-coding DNA across species

(Reproduced with permission from Mattick, et al., Nat Rev Genet 2004;5:316-323).

Figure 3. Pharmacogenetic expression quantitative trait loci (eQTL) study designs

Panel A: Sequential design - comparison of microarray expression profiles of responders (R) with non-responders (NR) for identification of pharmacogenetic candidates (Step 1), followed by eQTL mapping of candidate gene expression levels in a larger sample (Step 2). Significant eQTLs are subsequently carried to clinical cohorts for classical pharmacogenetic testing (Step 3). Panel B: Perturbation design - time-series experiment measuring global gene expression in response to drug administration (arrow), testing for SNP-specific differences in response phenotype (Step 1), which can then be carried forward to clinical testing in Step 2. These designs are not mutually exclusive. (Reproduced with permission from .)

Figure 4. Adaptations to Clinical Trials

A schematic diagram illustrating the basic structure of a clinical trial is shown in Panel A. Adaptations to clinical trials generally occur at the three levels depicted by the blue arrows shown in Panel B. The sources of information that drive a decision to adapt the trial vary and include data from an external source (Panel C), a prospectively planned analysis of interim data from the trial, and unplanned findings arising from an interim analysis (Panel D). The first two situations are referred to as a reactive revision (Panel C) and an adaptive design (Panel D), respectively.

Figure 4. Adaptations to Clinical Trials

A schematic diagram illustrating the basic structure of a clinical trial is shown in Panel A. Adaptations to clinical trials generally occur at the three levels depicted by the blue arrows shown in Panel B. The sources of information that drive a decision to adapt the trial vary and include data from an external source (Panel C), a prospectively planned analysis of interim data from the trial, and unplanned findings arising from an interim analysis (Panel D). The first two situations are referred to as a reactive revision (Panel C) and an adaptive design (Panel D), respectively.

Figure 4. Adaptations to Clinical Trials

A schematic diagram illustrating the basic structure of a clinical trial is shown in Panel A. Adaptations to clinical trials generally occur at the three levels depicted by the blue arrows shown in Panel B. The sources of information that drive a decision to adapt the trial vary and include data from an external source (Panel C), a prospectively planned analysis of interim data from the trial, and unplanned findings arising from an interim analysis (Panel D). The first two situations are referred to as a reactive revision (Panel C) and an adaptive design (Panel D), respectively.

Figure 4. Adaptations to Clinical Trials

A schematic diagram illustrating the basic structure of a clinical trial is shown in Panel A. Adaptations to clinical trials generally occur at the three levels depicted by the blue arrows shown in Panel B. The sources of information that drive a decision to adapt the trial vary and include data from an external source (Panel C), a prospectively planned analysis of interim data from the trial, and unplanned findings arising from an interim analysis (Panel D). The first two situations are referred to as a reactive revision (Panel C) and an adaptive design (Panel D), respectively.

Figure 5. The Learn-and-Confirm approach to drug development

First developed by Sheiner , this approach can be adapted to incorporate modern systems principles and network medicine to revise, make rational, and facilitate optimization strategies for drug development.

Figure 6. A novel model for clinical development

During the exploratory phase of development, this model uses all available knowledge and tools, including biomarkers, modeling, and simulation, as well as advanced statistical methodology. Trials are designed to determine proof-of-concept (POC) and to establish dose selection to a level of rigor that will enhance the likelihood of success in the confirmatory phase. During the confirmatory phase, modern designs, tools, and knowledge are applied to larger-scale studies with the goal of identifying the target patient population in which the drug is efficacious, establishing the benefit-to-risk ratio and confirming the optimal dose and dosing regimen. During this phase, innovative clinical trial designs, such as adaptive or seamless studies, compress timelines, improve dose and regimen selection, and reduce the number of patients assigned to non-viable dosing regimens. (Reproduced with permission from Orloff J, Douglas F, Pinheiro J, et al. The future of drug development: advancing clinical trial design. Nature reviews. Drug discovery. 2009;8:949-957)

Figure 7. Adaptive Dose-finding Study

In an adaptive dose-finding study, the dose assignment(s) to the next subject, or next cohort of patients, is based on responses of previous subjects, and the dose assignment is chosen to maximize the information about the dose–response curve, according to some pre-defined objective metric (e.g., variability in parameter estimates). In a traditional dose-finding trial, selecting a few doses may not adequately represent the dose–response relationship, leading many patients to be allocated to ‘non-informative’ doses (wasted doses), as shown in the figure. In adaptive dose-finding, the strategy is to include initially only a few patients on many doses to explore the dose-response, then to allocate the dose range of interest to a greater number of patients. This strategy reduces the allocation of patients to non-informative doses. Compared with fixed randomization, this approach has the ethical advantage that fewer subjects are assigned doses that are too high or too low; it can also avoid additional, separate trials that might be necessary when fixed dose-finding trials do not adequately define the dose range. Adaptive dose-finding trials also require an infrastructure that allows the rapid communication of responses from trial sites to a central unblinded analysis center and of adaptive dose assignments to the trial sites. Randomization software capable of rapidly computing dynamic allocation of doses to subjects is additionally mandated by adaptive trials because pre-specified randomization lists will not work. In addition, a flexible drug-supply process is required because demand for doses is not fixed in advance, but. Rather. evolves as information on responses at various doses is gathered as the trial progresses. (Modified with permission from Orloff J, Douglas F, Pinheiro J, et al. The future of drug development: advancing clinical trial design. Nature reviews. Drug discovery. 2009;8:949-957)

Figure 8. Theoretical Dose–toxicity Curves (DTCs) for continuous reassessment method with one patient per cohort

The solid line shows the prior dose-toxicty curve (DTC) from which the dose for the first patient is selected. With a desired dose limiting toxicity (DLT) rate of 0.25, the dose level for the first patient is level 4. The dashed line shows the estimated DTC after observing the first patient if the first patient experienced a DLT. If the first patient experienced a DLT at level 4, then patient 2 would receive dose level 3. The dotted line shows the estimated DTC if the first patient did not experience at DLT at level 4; patient 2 would receive dose level 5. (Reproduced with permission from Ivy SP, Siu LL, Garrett-Mayer E, Rubinstein L. Approaches to phase 1 clinical trial design focused on safety, efficiency, and selected patient populations: a report from the clinical trial design task force of the national cancer institute investigational drug steering committee. Clinical cancer research : an official journal of the American Association for Cancer Research. 2010;16:1726-1736.)