Getting Innovative Therapies Faster to Patients at the Right Dose: Impact of Quantitative Pharmacology Towards First Registration and Expanding Therapeutic Use

Abstract

Quantitative pharmacology (QP) applications in translational medicine, drug‐development, and therapeutic use were crowd‐sourced by the ASCPT Impact and Influence initiative. Highlighted QP case studies demonstrated faster access to innovative therapies for patients through 1) rational dose selection for pivotal trials; 2) reduced trial‐burden for vulnerable populations; or 3) simplified posology. Critical success factors were proactive stakeholder engagement, alignment on the value of model‐informed approaches, and utilizing foundational clinical pharmacology understanding of the therapy.

Model‐Informed Drug Discovery and Development: Current Industry Good Practice and Regulatory Expectations and Future Perspectives

“…An important part of this process is discussion of the aspects and factors that have led to success and failure of MID3 applications from both industry and regulatory perspectives and how greater global consistency can be achieved via early dialogue and parallel interactions with the EMA/FDA. With respect to the former, discussions on their role in bringing novel therapeutics faster to patients without increasing risk may help to convey importance of MID3 to decision makers . In addition, specific training of key stakeholders in industry and regulatory authorities, so that they can “trust” these approaches and how they are evaluated must be considered.…”

“…Model‐informed drug discovery and development (MID3) has been shown to play an important role in the efficient delivery of new therapies by both increasing the confidence in decision making across drug development and by eliminating costs or reducing cycle times . As part of this paradigm, quantitative modeling approaches are used across all phases of the drug development process: from biomarker selection in translational medicine to dose/regimen selection, evidence generation for regulatory approval, extrapolation to other disease areas or populations, and as an early input into pharmaco‐economic assessment .…”

Clinical Studies for the Sake of Negative Data: The Proof Is in the Pudding

“…Over the past 20 years, the chorus of voices advocating for greater use of model‐informed drug development (MIDD) and trumpeting its success has grown in volume, as regulatory agencies and the pharmaceutical industry have improved collaboration and aligned on a shared goal of greater efficiency and smarter decision making to improve attrition rates. A review of the literature reveals numerous examples of the successful use of modeling and simulation in all stages of drug development, from the selection of starting doses for first‐in‐human studies to exposure–response analyses and Bayesian adaptive designs for selecting phase III doses, to clinical trial simulation and lifecycle management . Recently, the US Food and Drug Administration (FDA) initiated a pilot program to facilitate the application of MIDD approaches with the goal of improving clinical trial efficiency, increasing the probability of regulatory success, and optimizing individualization of therapy…”

MID3: Mission Impossible or Model‐Informed Drug Discovery and Development? Point‐Counterpoint Discussions on Key Challenges

“…Numerous publications have extensively documented case studies demonstrating the impact of modeling and simulation (M&S) in decision making in industry, regulatory, and practice settings. [1][2][3][4][5][6][7] At the same time, there appears to be consensus within the community that the discipline needs to continue to evolve from one-off case studies to a paradigm of systematic, best practices-driven approaches to improving decision making across the DDRU continuum.…”

“…Their early experience has been encouraging and may ultimately help achieve transformative applications of MIDD approaches in drug development programs as a matter of routine. 10,11 While there is a general appreciation of the positive impact of MID3 on the quality and efficiency of decision making and its potential to have a significant impact on the well-documented research and development (R&D) productivity challenges [3][4][5][6] , MID3 in practice can be isolated and inconsistently applied across the community, with its full potential yet unrealized. We posit that this may be due to a number of factors ranging from unresolved scientific and technical issues, lack of standardized processes, operational and organizational barriers, and educational and knowledge gaps both within the scientific and clinical research community.…”