Model-based drug development (MBDD) is an approach that is used to organize the vast and complex data streams that feed the drug development pipelines of small molecule and biotechnology sponsors. MBDD relies on the construction of quantitative relationships to connect data from discrete experiments conducted along the drug development pathway. These relationships are then used to ask questions relevant at critical development stages, hopefully, with the understanding that the various scenarios explored represent a path to optimal decision making. The FDA critical path document characterizes MBDD as the development and application of pharmaco-statistical models of drug efficacy and safety from preclinical and clinical data to improve both drug development knowledge management and decision-making. Such data streams are ultimately reviewed by the global regulatory community as evidence of a drug’s potential to treat and/or harm patients. As MBDD becomes more integrated into the pharmaceutical research community, a more rational explanation for decisions regarding the development of new agents as well as the proposed treatment regimens that incorporate both new and existing agents can be expected. By providing quantitative justification for trial design, dose selection and decisions during trial execution, MBDD can improve the efficiency of clinical dev elopment. Quantitative clinical pharmacology can also boost the quality of a drug’s regulatory package. The concept of a MBDD paradigm is to construct quantitative expressions about target–drug activity drug-–exposure → exposure–(biomarker) response (efficacy/adverse events) → response–(clinical) outcome relationships, so that these questions promote assumption and scenario testing prior to clinical investigation.
Some examples of the application of MBDD at different stages of drug development include:
Drug Candidate Selection:
Drug discovery and development involve the utilization of in vitro and in vivo experimental models. Different models, ranging from test tube experiments to cell cultures, animals, healthy human subjects, and even small numbers of patients that are involved in clinical trials, are used at different stages of drug discovery and development for determination of efficacy and safety. The proper selection and applications of correct models, as well as appropriate data interpretation, are critically important in decision making and successful advancement of drug candidates.
Simcyp’s (Simulators) R&D activities focus on the development of algorithms along with population and drug databases for modelling and simulation (M&S) of the absorption and disposition of drugs in patients and specific subgroups of patients across different age/sex ranges. Simcyp’s allow predicting the drug absorption, distribution, metabolism and excretion and potential drug-drug interactions. The Simcyp models use experimental data generated routinely during pre-clinical drug discovery and development from in vitro enzyme and cellular systems, as well as any relevant physico-chemical attributes of the drug and dosage forms. A model-based approach can utilize available in vitro and/or in vivo data to predict the pharmacokinetic profile of a drug in humans prior to the first human exposure. These early predictions can be a key component in the rationale for selecting the first dose to administer to humans. Specifically, doses can be selected which are predicted to provide an acceptable safety margin relative to exposures achieved in non-clinical toxicology studies.
Phase 1 Clinical Development:
Early human PK or PD data can be used to develop the next stage of models of human exposure and/or PD response. A real-time model-based approach may be particularly useful to guide dose escalation during the conduct of ascending-dose studies. Upon completion of these studies, simulations based on the final model(s) can be a valuable resource when designing and optimizing longer-term studies.
Phase 2 Proof of Concept:
Data collected at the proof of concept stage can be used to develop ever more robust models. At this stage of development model-based predictions can be critical to selection of study designs, optimal doses, and dosing regimens to progress into Phase 3.
Phase 3 Clinical Development:
At this stage in development PK and PD (biomarkers) data are typically collected in a broad sample of the target population. These data allow further development of PK and PD models in preparation for regulatory filing and marketing. A key aspect of this stage of MBDD is characterizing the variability in drug concentrations and drug response. Identification of clinically relevant demographic factors (e.g., age, body weight, renal and hepatic functions) that impact variability is often a critical step in development of these models. Information gleaned from these models often serves as a foundation for developing dosing guidance in special populations (renal/hepatic impairment), age groups (elderly/children) or based on other clinically relevant factors identified in the model.
MBDD is a tool that is increasingly used throughout the drug discovery and development continuum to support fast and rationale decision making and has thereby the potential to accelerate and increase the cost-effectiveness of the drug development process. The use of suitable biomarkers (PD markers) in MBDD, has shown its merits in therapeutic areas, especially in early clinical development.