Industrial Perspective on the Benefits Realized From the FDA’s Model‐Informed Drug Development Paired Meeting Pilot Program

Galluppi, Gerald R.; Brar, Satjit; Caro, Luzelena; Chen, Yuan; Frey, Nicolas; Grimm, Hans Peter; Rudd, Deanne Jackson; Li, Chi-Chung; Magee, Mindy; Mukherjee, Arnab; Nagao, Lee M.; Purohit, Vivek S.; Roy, Amit; Salem, Ahmed Hamed; Sinha, Vikram; Suleiman, Ahmed A.; Taskar, Kunal S.; Upreti, Vijay V.; Weber, Benjamin; Cook, Jack

doi:10.1002/cpt.2265

Cited by 17 publications

(23 citation statements)

References 3 publications

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“…On behalf of the IQ Consortium, Galluppi et al . review the benefits gained from the FDA’s MIDD Paired Meeting Pilot Program 25 . While this avenue for regulatory consultation was leveraged from preclinical through postmarketing stages of development, approximately two‐thirds of applications were in phases I–II, consistent with the value that would be gained in early regulatory alignment to inform an efficient and appropriately de‐risked pivotal/confirmatory development.…”

Section: Impactful Midd Relies On Interdisciplinary Collaborations An...mentioning

confidence: 94%

Model‐Informed Drug Development: Connecting the Dots With a Totality of Evidence Mindset to Advance Therapeutics

Venkatakrishnan

Graaf

2021

Clin Pharma and Therapeutics

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The Merriam-Webster dictionary offers the essential meaning of a model as "a usually small copy of something," or "a set of ideas and numbers that describe the past, present, or future state of something." Model-informed drug development (MIDD) involves the strategic creation and integration of mathematical models for knowledge management, predictions, and decision making in pharmaceutical research and development. These models can "connect the dots" across various inputs related to drug properties, disease biology, patient characteristics and trial designs, transforming data to knowledge, enhancing efficiency in drug discovery and development, and maximizing therapeutic potential. This issue of Clinical Pharmacology and Therapeutics (CPT) includes Original Research Articles, Reviews, and Perspectives illustrating recent advances in established and emerging areas of MIDD, highlighting opportunities for interdisciplinary and cross-sector collaboration for enhancing impact.

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Section: Impactful Midd Relies On Interdisciplinary Collaborations An...mentioning

confidence: 94%

Model‐Informed Drug Development: Connecting the Dots With a Totality of Evidence Mindset to Advance Therapeutics

Venkatakrishnan

Graaf

2021

Clin Pharma and Therapeutics

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“…While for MIDD, commonly data-driven or phenomenological models are used, more complex and multiscale models are coming of age and get submitted to regulators . Data from relevant MSM tools to refine, reduce or even replace trials, could provide additional economic incentives to sponsors (Galluppi et al, 2021). Likewise, however, especially complex models and MSMs models that do not meet the FDA requirements for credibility of model data, will fail to be considered.…”

Section: Credibility Of Modelsmentioning

confidence: 99%

Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility

Karr¹,

Malik-Sheriff²,

Osborne³

et al. 2022

Front. Syst. Biol.

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During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.

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“…Model projected therapeutic dose is one of the many candidate selection criteria commonly used in drug discovery. 11 , 12 It is a multidimensional metric that requires inputs from multiple scientific disciplines in drug research and development. The patient population and special population characteristics, physicochemical properties of the molecules (e.g., FcRn binding and AC‐SINS score, further information is listed in the Supplementary Materials ), formulation characteristics (e.g., solution concentration, number, and volume of injections), pharmacokinetic (PK), and pharmacodynamic (PD) properties are all important criteria in the selection of the optimal neutralizing antibody and the dose.…”

Section: Figurementioning

confidence: 99%

A Quantitative Modeling and Simulation Framework to Support Candidate and Dose Selection of Anti‐SARS‐CoV‐2 Monoclonal Antibodies to Advance Bamlanivimab Into a First‐in‐Human Clinical Trial

Cosson

Jordie

Riggs

et al. 2021

Clin Pharma and Therapeutics

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Neutralizing monoclonal antibodies (mAb), novel therapeutics for the treatment of coronavirus disease 2019 (COVID‐19) caused by severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2), have been urgently researched from the start of the pandemic. The selection of the optimal mAb candidate and therapeutic dose were expedited using open‐access in silico models. The maximally effective therapeutic mAb dose was determined through two approaches; both expanded on innovative, open‐science initiatives. A physiologically‐based pharmacokinetic (PBPK) model, incorporating physicochemical properties predictive of mAb clearance and tissue distribution, was used to estimate mAb exposure that maintained concentrations above 90% inhibitory concentration of in vitro neutralization in lung tissue for up to 4 weeks in 90% of patients. To achieve fastest viral clearance following onset of symptoms, a longitudinal SARS‐CoV‐2 viral dynamic model was applied to estimate viral clearance as a function of drug concentration and dose. The PBPK model‐based approach suggested that a clinical dose between 175 and 500 mg of bamlanivimab would maintain target mAb concentrations in the lung tissue over 28 days in 90% of patients. The viral dynamic model suggested a 700 mg dose would achieve maximum viral elimination. Taken together, the first‐in‐human trial (NCT04411628) conservatively proceeded with a starting therapeutic dose of 700 mg and escalated to higher doses to evaluate the upper limit of safety and tolerability. Availability of open‐access codes and application of novel in silico model‐based approaches supported the selection of bamlanivimab and identified the lowest dose evaluated in this study that was expected to result in the maximum therapeutic effect before the first‐in‐human clinical trial.

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Industrial Perspective on the Benefits Realized From the FDA’s Model‐Informed Drug Development Paired Meeting Pilot Program

Cited by 17 publications

References 3 publications

Model‐Informed Drug Development: Connecting the Dots With a Totality of Evidence Mindset to Advance Therapeutics

Model‐Informed Drug Development: Connecting the Dots With a Totality of Evidence Mindset to Advance Therapeutics

Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility

A Quantitative Modeling and Simulation Framework to Support Candidate and Dose Selection of Anti‐SARS‐CoV‐2 Monoclonal Antibodies to Advance Bamlanivimab Into a First‐in‐Human Clinical Trial

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