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Nonclinical Modeling Leads to Smarter Drug Development

At the point of translation from preclinical to clinical development, the critical question is: “Will this molecule test the hypothesis?

Hypothesis:  If target engagement = x, the response will = y. One clinician scientist once said, “The only reason for preclinical development is to make sure you have some pretty figures in the investigator’s brochure (IB).”

It’s true, translation from preclinical to clinical development is a tricky business.  So why do we spend so much time and effort in preclinical development optimizing and selecting the best possible molecules to progress into the clinic if we’re only generating “pretty figures” and not answering the critical question?

Answering the critical question

Advancing a molecule into the clinic that cannot achieve sufficient target engagement within a reasonable margin of safety is the path to attrition. In order to answer the critical question, “will this molecule test the hypothesis?”, these three basic principles about the candidate must be understood:

  • Level and duration of target engagement required for efficacy
  • Exposure of the candidate molecule required to achieve target engagement
  • Margin between number (2) and safe exposure to the drug

Modeling in Nonclinical Drug Development

Modeling and simulation has become a common practice in clinical development and has progressed from gaining acceptance by regulatory agencies to becoming an expectation. However, the same modeling and simulation concepts used in clinical development can be utilized to provide a data driven answers to the critical question during the transition from preclinical to clinical development.

Model-based approaches are developed early on in drug discovery to validate animal models or to identify potential biomarkers for efficacy or safety.  These models can be used to quantitatively optimize or select lead candidate molecules.  As programs progress, nonclinical PKPD models can be expanded to guide first in human (FIH) doses by predicting human PK and pharmacology to determine the minimal anticipated biological effect level (MABEL).

Often, nonclinical PKPD models can identify important early indicators of target engagement or safety signals that can be utilized to gain more value from early clinical trials.  As clinical data is obtained, nonclinical models can be refined to identify potential development hurdles such as potential DDI, cQT or PK in special populations.

When it comes to modeling in drug development, earlier is better and essential to putting meaning behind those “pretty figures” in the investigator’s brochure.  The biggest mistake in drug development is progressing a molecule that will never have the ability to “test the hypothesis.”

PK/PD models can be used in Nonclinical studies for the following:

  • Evaluate efficacy-safety profile – candidate selection (PK/PD models)
  • Allometric scaling – Animals to Humans
  • Evaluate potential QT prolongation
  • FIH dose and dosing schedule – Maximum Recommended Starting Dose (MRSD FDA Guidance Document)
  • Predicting thyroid hormone side effects in Humans from Toxicology studies
  • Predicting GP120 inhibitor for go/no-go decisions

Conclusions

Modeling and simulation is a common practice in Phase 2 and proof of concept studies. It is not commonly visited in the early stages of drug development. However, there are many reasons to conduct modeling and simulation during pre-clinical development. In this blog post, we outlined many of the benefits of Nonclinical Model Based Drug Development (MBDD).

Modeling and simulation in preclinical development focuses on the translation of preclinical data into quantifiable predictions of the pharmacokinetics, pharmacology (proof of mechanism and concept), and safety in man. You can determine which compounds have the best potential for success.  This enables the selection of drug candidates with the best efficacy and safety profile for clinical development and the optimization of first time in human (FTIH) clinical trial designs.

Model-based approaches 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.


Contact us to learn more about how Model Based Drug Development (MBDD) can benefit your nonclinical program

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