Essentials of Model-informed Drug Development in Oncology

Model–informed drug development (MIDD) is critical to informing the development of safer and more effective new drugs. Indeed, all drug discovery and development today involve some level of MIDD, which encompasses a range of applications. This blog will focus on best practices for model-informed drug development in oncology. 

MIDD maximizes and connects the data collected during non-clinical and clinical development and real-world settings, consisting of individual-level and summary-level information. Learn more about our real-world evidence services here. 

It enables extrapolation of that data to unstudied situations and populations to characterize potential risks, mitigate them, and increase the probability of success. Using MIDD can allow bridging across development phases and increase certainty, and propagate knowledge in early development planning, allowing acceleration of timelines. 

MIDD has become an established approach in the last decade. However, pharmacokinetic/pharmacodynamic (PK/PD) and population PK (PopPK) modeling and simulation (M&S) techniques were introduced over 30 years ago. In the 90s, they were largely used experimentally to support drug development programs but weren’t pivotal to decision-making. From 2000 to 2010, using PopPK and PK/PD became embedded in drug development and is now critical to many international regulatory guidance documents and frameworks. In addition, global regulatory agencies also encourage integrating MIDD approaches into drug submissions. 

Understanding how an investigational drug’s safety and efficacy profile compares to the standard of care and/or competitor drugs is important medically and commercially. 

Model-based meta-analysis (MBMA) uses highly curated clinical trial data in databases and pharmacometrics 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 support designing and executing pivotal studies. In some cases, MBMA models can even serve as an external control arm. 

Large pharma adopted mechanistic modeling approaches such as physiologically-based pharmacokinetic (PBPK) modeling and simulation, quantitative systems pharmacology (QSP), and semi-mechanistic PK/PD modeling as experimental techniques in the 90s. Nowadays, PBPK modeling to predict drug-drug interactions (DDIs) and explore dosing in special populations, such as pediatrics, has become the pharmaceutical industry standard. These models are also frequently used to predict PK in unstudied populations. 

In recent years, the use of QSP has increased. QSP combines computational modeling and experimental data to examine the relationships between a drug, the biological system, and the disease process. 

MIDD can be applied to all therapeutic areas. This blog highlights its application to oncology drug development. This approach can inform all stages of cancer drug development from early to late clinical development. It allows us to understand an investigational oncology drug’s PK, safety, and efficacy, first in nonclinical species. Then this information can be translated 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. Concentration-ECG (e.g., QTc) analysis using early-stage clinical trial PK-matched QTc data can de-risk the cardiac safety risk or waive the need for a clinical trial to provide justifications.  

Oncology drugs are often combined with novel compounds or the standard of care. However, choosing the optimal drug combinations is difficult using conventional clinical trial designs. 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 support the optimal trial design and to understand the competitive landscape.  

Ultimately, using MIDD approaches can save time and money while increasing drug efficacy and minimizing risk to the patients.  

** 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.