Quantitative pharmacology encompasses the many strategic advantages of using complex mathematical models to understand biochemical relationships that ultimately improve clinical decision-making. This includes pharmacometric modeling, familiar to those who have used pharmacokinetic/pharmacodynamic (PK/PD) modeling to improve dosage decisions, and the extension of such models to the performance of meta-analyses, the construction of decision rules, and other uses involving a broad array of cases. In this blog we summarize some key areas of opportunity.
Exposure Response Pharmacometric models are perhaps the most well-known elements of quantitative pharmacology. Pharmacometric methods aim to create models that connect the presence of a new pharmacological agent with the body’s response to the new agent. Observations on exposure, also known as pharmacokinetics (PK), can include quantifying metrics that describe the body’s exposure to the medicine, such as the time taken for a new medicine to be absorbed, where and how quickly it gets distributed throughout the body, and general details about “what the body does to the drug” to remove it from the blood stream. In addition to “exposure,” another factor that can be measured is “response,” which provides an account of how a drug affects the body. The study of the response is referred to as pharmacodynamics (PD). Together, a PK/PD model can provide a clear sense of how the new drug and the body act as a system to target certain illnesses.
Inform Dose Selection The most straightforward use of PK/PD models is dose selection in early phases for optimal late-phase success. When choosing doses for measuring safety, a common rule to ensure limited harm from toxicity might be a rule such as 3+3, in which dose intensity is increased if three patients respond successfully to the dosage (see the section on dose-response modeling for more information). These rules are easy to implement, but are quite elementary. A well-developed PK/PD model can provide more precise guidance beyond simply recommending an increase or decrease in the dosage after responses from a certain number of patients. Most PK/PD models incorporate temporal designs, which enable decision-makers to understand the timing and magnitude of clinical response with regard to the presence of the drug. This knowledge is often incorporated in future study designs to ensure that efficacy is measured at the correct time, thus facilitating a potential reduction in sample collections.
Models that describe the magnitude of clinical response are often used not only to identify optimal dose ranges, but also to identify subsets of patients for whom the drug may have profoundly different responses and for whom early dose adjustment may be required. This can yield functional benefits such as shortening the length of a Phase 2 trial without compromising the chances of getting the Phase 3 dosage correct, as well as helping to guide Phase 3 decisions if dosage needs to be adjusted (e.g., when a particular subpopulation might require further study or when unanticipated side-effects are newly detected). An accurate model provides clearer details about how to adjust doses to obtain more precise data for later phases of a trial.
Performance of Meta-Analyses Another feature of quantitative pharmacology is the performance of meta-analyses that combine evidence from numerous trials within a therapeutic area. Just as models can be built using evidence gleaned from a trial, pharmacometric models can be built from data gathered across numerous trials. In this setting, summary-level data reported in published trials is used in place of individual-level data from a single trial. These meta-analyses ensure that trial design and implementation takes into account the findings of all the published trials, and not simply those that are a part of the investigator’s clinical development program. In general, models, either built through PK/PD analysis or from industry meta-analysis, are tools that decision-makers can use for a variety of purposes. While dose selection is a common functional benefit of modeling early phase data, other uses include selecting primary and surrogate endpoints, exploring new endpoints, and determining go/no-go decision rules for later-phase trial design.
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