Model-Based Drug Development (MBDD) is a process where drug development decisions
are supported by a mathematical simulation (or model) that determines the likelihood of
success for a certain drug.
By using mathematical and statistical methods, we build models of drug concentrations (pharmacokinetic) and/or drug responses (pharmacodynamic) over a time course (PK/PD).
Such models allow one to understand how various dosing choices (e.g., dose amount, dose frequency, & dosing duration) can affect drug concentrations while also elucidating the relationship between drug concentration and the desired or undesired pharmacodynamic responses. In addition, these models help to characterize the PK/PD variability of drugs and assist in understanding the clinically relevant factors contributing to variability (for example:
age, body weight, renal function, hepatic function).
Minimize your Risks and Costs by Maximizing Understanding
Developing a drug can become progressively more and more expensive. If MBDD t is the right choice for your drug development program, Nuventra can help you save large amounts of time, money and resources.
MBDD can be especially useful if you already have data for your drug. Our PK/PD and Modeling Experts can take existing data that you already have and predict additional human responses. For example:
- Nuventra can use MBDD to predict how the drug will react in different populations.
- If you have enough data from other studies – you can save yourself time and money by avoiding a full QT study by using a cQT Model instead.
- If you have data from a single does study, we can use modeling to predict responses for multiple doses. This is important because our team will use MBDD to help you justify the dose that you are proposing for a multiple dose study by using the data from the already completed single dose study.
MBDD can play a critical role throughout the drug development process. Some examples of the application of MBDD at various stages of drug development include:
- Drug Candidate Selection (Pre-IND): 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.
- Early Clinical Development (Phase 1): Early human pharmacokinetic (PK) or pharmacodynamic (PD) data can be used to develop the next stage of models of human exposure and/or pharmacodynamic 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.
- Proof of Concept (Phase 2): 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 III.
- Phase 3: At this stage in development pharmacokinetic (PK) and pharmacodynamic (PD) 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 function, hepatic function) 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.
- Evaluations and comparison trial designs based on simulation to enhance the likelihood of a successful outcome.
- Creation of tailored MBDD plans to guide MBDD strategy throughout the drug development process.
- Industry leading pharmacokineticists that are able to provide superior advice for modeling PK and pharmacokinetic/pharmacodynamic relationships.
- Rational selection of the most salient model(s) for a given investigational compound and disease area.
- Innovative methods and ideas, including the development of a revolutionary new algorithm for efficient population pharmacokinetic/pharmacodynamic (PK/PD) analysis by Mark Sale, MD (Vice President, Modeling & Simulation and Executive Consultant, Population PK)
Clinical trial simulation is used to better predict how a drug might behave in a given clinical trial. Simulations can be used to perform “virtual clinical trials” and provide insight into multiple aspects of the trial, including sample size power estimation, concentration-response behavior, the interplay of key parameters within a given patient population, estimation of uncertainty/risk for the trial, etc.
- Nuventra can assess different scenarios for clinical trials to increase the probability of success for a given drug or study design.