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How to Form a Clinical Pharmacology Strategy for your NDA, BLA, or MAA

It’s common knowledge that every approved drug on a pharmacy shelf must undergo extensive testing to demonstrate safety and efficacy. However, there is much more that goes into the approval of a drug. Every drug also goes through an extensive review of clinical pharmacology data. If you look closely at a drug’s package insert (that tightly wadded piece of paper available for each prescription drug), about 25% of the data included can be traced back directly to clinical pharmacology.

The FDA and other regulatory agencies around the world require clinical pharmacology science to approve drugs and biologics. Pharmacokinetics (PK), pharmacodynamics (PD), and pharmacometrics (also known as modeling & simulation) fall under the clinical pharmacology umbrella. PK, PD, and modeling approaches such as population PK (popPK) analyses are used to understand the absorption, distribution, metabolism, and excretion (ADME) characteristics of drugs and how they behave in different patient populations.

It is critical to develop a clinical pharmacology strategy early in drug development for new therapeutics to support a new drug application (NDA), biologic license application (BLA), or marketing authorization application (MAA).

Nonlinear Clinical Pharmacology Strategy Approach

Many people mistakenly believe that clinical pharmacology occurs only in Phase 1. Another common misconception is that all drug development programs progress linearly through Phase 1, 2, and 3 (meaning that once Phase 1 is completed, you then move to Phase 2 and so on). In actuality, clinical pharmacology can be applied across all Phases of clinical drug development (Phases 1 to 4) and can occur in a nonlinear fashion.

The concept of a nonlinear clinical pharmacology strategy can also be described as being “bootstrapped” whereby existing data is used to make informed decisions about next steps in the program. The concept also applies a “goldilocks paradigm” whereby the optimal amount of data is produced to support an NDA, BLA, or MAA; no more and no less.

This “bootstrapped goldilocks” paradigm can be accomplished with a prospectively planned and minimalistic clinical pharmacology program with built-in feedback loops that initiate clinical pharmacology studies only when needed as the pharmaceutical asset reaches value inflection points during development. A value inflection point justifies committing additional funds and resources towards the overall program.

“Bootstrapped Goldilocks” Strategy

An example of this overall concept is as follows: a pharmaceutical asset generates enough preclinical data to support an investigational new drug (IND) application and an initial Phase 1 first-in-human (FIH) single ascending dose (SAD) study and a multiple ascending dose (MAD) study. These initial Phase 1 clinical pharmacology studies are designed specifically to generate information in support of a recommended Phase 2 dose and assist in designing a Phase 2 clinical strategy.

Additional clinical pharmacology studies needed to complete an NDA, BLA, or MAA are put on hold until a safety or efficacy signal is generated in Phase 2 clinical studies (i.e., a value inflection point). As more data in Phase 2 are generated, additional Phase 1 clinical pharmacology studies and analyses are conducted.

Clinical pharmacology studies to consider as the asset generates Phase 2 clinical efficacy and safety data include: hepatic or renal impairment, drug-drug interaction, and modeling and simulation/population PK. This additional body of evidence is used to design and justify the Phase 3 clinical program. Additional value inflection points achieved in Phase 3 can initiate further Phase 1 clinical pharmacology studies and supplementary modeling and simulation analyses such as:

Most drug development programs require two adequate and well controlled Phase 3 studies. Generally, there is sufficient time in Phase 3 to complete the necessary clinical pharmacology studies needed to finalize sections 2.7.1 and 2.7.2 of an NDA, BLA, or MAA. It is important to use the data collected during Phase 1 and 2 to help inform when and which clinical pharmacology studies to perform in Phase 3.

Understanding this “bootstrapped goldilocks” clinical pharmacology strategy and which investigations you need or don’t need and which investigations to do earlier versus later is vital for your program’s overall strategy and will help you obtain the optimal amount of data (no more and no less).

Applications of Clinical Pharmacology

Clinical pharmacology is used to understand how a drug interacts with the human body. The applications of clinical pharmacology can be used at every stage of drug development and help answer important questions about a drug’s safety, PK, PD, and more. Some additional applications of clinical pharmacology include:

  • informing clinical efficacy and safety trials
  • informing dose selection and justification
  • avoiding certain clinical studies that may be unnecessary 
  • de-risking certain characteristics of a drug, thereby increasing the valuation of the asset
  • Addressing known therapeutic class, compound-specific, and indication-specific issues that might be questioned during investor due diligence


A nonlinear, “bootstrapped goldilocks” clinical pharmacology strategy requires careful and prospective planning as well as frequent regulatory interactions validating the strategy based on the body of evidence generated during development. Clinical pharmacology programs vary and should be tailored specifically for each drug.

Our clinical pharmacology experts at Nuventra can help develop a clinical pharmacology strategy that is tailored specifically for your program. It is important to generate the right amount of clinical pharmacology data at the right time. Having a strong clinical pharmacology strategy can not only save valuable time and resources but is also vital to achieving regulatory approval. Contact us to learn how Nuventra can help support your clinical pharmacology strategy and other drug development needs.

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