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Essentials of Model-informed Drug Development (MIDD) – Top-down vs. Bottom-up Approaches

How can the latest modeling and simulation technologies accelerate drug discovery and development?

We frequently receive questions about why model-informed drug development (MIDD) is so critical to informing the development of safer and more effective new drugs? Indeed, all drug discovery and development today involves some level of MIDD, a term that encompasses a large range of applications.

The answer we provide is that MIDD maximizes and connects the information that you can obtain from all the data that you have gathered about a medicine during development. It then enables you to extrapolate that data to unstudied situations and populations to characterize potential risks and develop a strategy to mitigate those risks and increase the probability of success. In some cases, the use of MIDD allows you to bridge across development phases or have better certainty in early development planning allowing you to accelerate timelines.

Top-down (empirical MIDD approaches)

MIDD has really taken off in the last decade, but it started over 30 years ago with the introduction of the concepts of pharmacokinetic/pharmacodynamic (PK/PD) and population PK (PopPK) modeling and simulation (M&S). In the 90s, it was largely used experimentally to support drug development programs but was not pivotal to decision making. From 2000 to 2010, the use of PopPK and PK/PD became embedded in drug development and is now a critical part of many international regulatory guidance documents and frameworks.

Understanding how an investigational medicine’s safety and efficacy profile compares to the standard of care and/or competitor drugs in development is important both medically as well as commercially. Model-based meta-analysis (MBMA) uses highly curated clinical trial data and pharmacology models to enable indirect head-to-head comparison, considering the impact of treatment, dosing regimen, patient population, and trial characteristics on responses to medicines. This can be used to support the design and execution of pivotal study designs, in some cases even acting as an external control arm.

Bottom-up (mechanistic MIDD approaches)

Originally developed in the 1930s, large pharma adopted physiologically-based pharmacokinetic (PBPK) modeling and simulation as an experimental technique in the 90s. Nowadays, PBPK modeling to predict drug-drug interactions (DDIs) and explore dosing in special populations, such as pediatrics, has become the standard within the industry and is frequently used to predict PK in unstudied populations.

In recent years, we have seen the increased use of quantitative systems pharmacology (QSP), which combines computational modeling and experimental data to examine the relationships between a drug, the biological system, and the disease process.

From “nice to have” to “regulatory essentials”

In recent decades, PopPK, PK/PD, and exposure-response MIDD have become expected components for late-stage clinical drug development programs. These “pharmacometric” approaches are also accepted as standard regulatory development tools and part of the strategic planning and drug development plans. These activities are no longer considered a nicety, but rather a necessity. Regulatory agencies expect that you have considered the use of these tools throughout drug development and a product’s life cycle management.

MIDD is not just used to improve decision making. It is also increasingly used to improve the probability of success of development programs, to optimize clinical study designs, and even to avoid the need to conduct certain clinical studies.

MIDD Informs Oncology Drug Development

While MIDD can be applied to all therapeutic areas, this blog will focus on its application to oncology drug development. This approach can inform all stages in oncology development from early to late clinical development. It allows us to understand an investigational oncology drug’s PK, safety, efficacy, first in nonclinical species, and then use this information to translate to first-in-human studies where it can inform the dosing strategy (dose, dosing intervals, dose schedule, and dose escalation/de-escalation plan). In addition, PK/PD modeling can help characterize the impact of intrinsic (age, weight, sex, genetic factors, etc.) and extrinsic (food, co-medications, smoking status, etc.) covariates on drug exposure.

Oncology drugs are often given as combination regimens with novel compounds or the standard of care. But choosing the optimal combination of drugs is not easy to assess through conventional clinical trials. Emerging mechanistic approaches such as mechanistic compartmental and QSP modeling have enabled us to assess downstream biochemical and cellular effects of modulating the target pathway to support developing a drug combination and safety strategy. Lastly, MBMA can be used to help to support the optimal trial design and to understand the competitive landscape. Ultimately, wise use of various MIDD approaches can save time and money while increasing drug efficacy and minimizing risk to the patients.

Comment from Professor Andrew McLachlan, The University of Sydney:

Modeling and simulation approaches are reshaping medicine’s development and regulatory science. The technology and innovation to support model-informed drug development continues at a pace, with many outstanding case studies of pharmacometrics approaches streamlining the path to market, reducing the costs of drug development, and reducing the timeframe for decision making. MIDD has significant benefits in repurposing medicines and designing dose regimens in patient populations where it is ethically or logistically difficult to conduct clinical trials. The next challenge is to continue to build the workforce of talent with the skills and expertise to deliver the benefits in MIDD in the sector.

Comment from Professor Carl Kirkpatrick, Monash University:

MIDD is an essential part of drug discovery and development with proven benefits in decision making, speed to market, and reducing drug development costs.  These observable drug development benefits align with other industry sectors, such as aerospace, who have utilized and reaped the benefits of modeling and simulation for decades.  Looking forward, undergraduate and post-graduate education and training in MIDD skills, along with the early implementation of MIDD approaches in discovery programs in academia and institutes, are essential to ensure Australia remains at the forefront of translatable drug discovery and development.

** The Essentials of model-informed drug development (MIDD) top-down versus bottom-up approaches webcast was held in partnership with AusBiotech, the leading Australian industry body representing and advocating for organizations doing business in and with the global life sciences economy. BiotechTalks, the AusBiotech virtual events platform, allows the Australian biotechnology industry to stay connected, present ideas, projects and updates, and exchange knowledge from home office to home office.

To learn how Certara can help you with model-informed drug development consulting, read more here.

About the authors

Amy Cheung, PhD
By: Amy Cheung, PhD

Dr. Cheung has over a decade of experience working in the pharmaceutical industry at AstraZeneca, with her role as Senior Pharmacometrician and Project manager of AZ Paediatric working group. She obtained her Ph.D. from the University of Manchester, on the topic of Structural Identifiability Analysis in Pharmacokinetic and Pharmacodynamic Models.

Fran Brown, PhD
By: Fran Brown, PhD

Fran has over 25 years of experience with strategic and operational global drug development from early discovery to filing and post-marketing.  She possesses a broad knowledge of strategic drug discovery and development, with a special focus on development strategy and the application of model-informed drug development (MIDD).