Antibody Drug Conjugates (ADCs) are constructed by attaching a small molecule drug to an antibody via a linker. The antibody selectively targets tumor cells and releases the cytotoxic drug within the cells to kill cancerous cells while sparing healthy tissue. Although some ADCs have been approved, many unanswered questions remain, such as drug-drug interactions (DDIs) and dose-limiting toxicity. In this blog post, I’ll discuss how PBPK models can help characterize the pharmacokinetics of ADCs.
The landscape for ADCs
Today, most pharma companies that have a biologics program also have an ADC one. Currently, there are three Food and Drug Administration (FDA) approved ADCs, around 40 in clinical trials, and a lot more in research. However, only two of them—Adcetris and Kadcyla—are currently on the market. This new, scientifically-complex therapeutic approach is expected to dramatically impact the oncology market. According to the report, “Global Antibody Drug Conjugate Market Outlook 2020,” the ADC market is anticipated to reach around $12.7 billion by 2020.
Conducting studies in virtual patients to predict pharmacokinetics/pharmacodynamics
Certara’s Simcyp Simulator is a sophisticated PBPK modeling and simulation platform, which is used to determine first-in-human dose selection, predict DDIs, understand drug disposition in special populations, including pediatric patients, and bridge to virtual ethnic populations. The Simcyp Simulator has increasingly been used to inform drug label language, and has regulatory acceptance from the US FDA, the European Medicines Agency, and the Japanese Pharmaceuticals and Medical Devices Agency.
The Simcyp whole body simulation methodology can predict the pharmacokinetics and pharmacodynamics of small molecule and biological medicines using laboratory-derived data. The Simulator includes a unique set of genetic, physiological and epidemiological databases that facilitate simulating virtual populations of differing demographics and ethnicities.
Supporting mechanistic-modeling of ADCs
PBPK modeling of both sides of an ADC—the large antibody molecule and the small cytotoxic ones—simultaneously had not been done before. Certara’s new Simcyp Simulator ADC model was developed at the request of the Simcyp Consortium. This new Simcyp PBPK module allows researchers to study different populations of virtual subjects to determine how they are likely to respond to a specific ADC. These data are especially important with oncology patients who often take multiple drugs concurrently. Some of these chemotherapy drugs could inhibit the clearance of the toxic small molecules released by the ADC, leading to higher than expected exposure and potential adverse events, and thus warranting DDI study of ADCs.
This new ADC module in the Simcyp Simulator facilitates mechanism-driven modeling, characterization and simulation studies of pre-clinical ADCs and DDIs. Both large antibody molecules and small cytotoxic drug molecules are modeled simultaneously in an integrated PBPK approach. This tool includes deconjugation models of ADC species in vivo, a mechanistic model for ADC or antibody penetration into the tumor, target-mediated drug disposition models in the plasma, lymph node, normal tissue and tumor, a permeability-limited tumor model for released payloads with uptake and efflux transporters, and a model showing delayed release of payloads. In addition, ADC clearance at various sites in the body and associated payload release can be traced to evaluate dose-limiting toxicity.
Helping to bring better oncology treatments to patients
This new model is currently being used by Simcyp Consortium members. As the market grows, ADC programs will need sophisticated, targeted models to help characterize their drug’s disposition and predict potential DDIs. The additional data provided by this ADC model will allow sponsors to refine their clinical trial designs and drug development programs. It could also help to reduce drug development costs as PBPK modeling has been shown to obviate the need for additional clinical trials in some instances.
The Simcyp Simulator has been trusted by sponsors and global regulatory agencies for many years. It is regularly employed to inform drug label language to benefit both patients and payers. As a result, Simcyp Consortium members asked Certara to build on its existing PBPK expertise to develop a model that would allow them to analyze the impact of both the large and small molecules within the ADC on the body. A complex model of this type had not been created previously. Certara’s Simcyp team successfully developed a tool that facilitates modeling, characterization and simulation studies of pre-clinical ADCs and DDIs in a more mechanistic manner. It should help bring safer, more efficacious ADCs to market.
Yuan, C., Samineni, D., Mukadam, S., Wong, H., Shen, B.Q., Lu D., Girish, S., Hop, C., Jin J.Y., Li, Chunze. Physiologically Based Pharmacokinetic Modeling as a Tool to Predict Drug Interactions for Antibody-Drug Conjugates. Clin. Pharmacokinet. DOI 10.1007/s40262-014-0182-x.
Gill KL, Gardner I, Li L, and Jamei M (2015) A Bottom-Up Whole-Body Physiologically Based Pharmacokinetic Model to Mechanistically Predict Tissue Distribution and the Rate of Subcutaneous Absorption of Therapeutic Proteins. AAPS J (ahead of print). http://www.ncbi.nlm.nih.gov/pubmed/26408308
Chetty, M., L. Li, R. Rose, K. Machavaram, M. Jamei, A. Rostami-Hodjegan, and I. Gardner, Prediction of the pharmacokinetics, pharmacodynamics and efficacy of a monoclonal antibody, using a physiologically based pharmacokinetic FcRn model., Frontiers in Immunology, 2014.http://journal.frontiersin.org/Journal/10.3389/fimmu.2014.00670/abstract
Li, L., I. Gardner, M. Dostalek, and M. Jamei, Simulation of Monoclonal Antibody Pharmacokinetics in Humans Using a Minimal Physiologically Based Model, AAPS J, 2014. (This paper was one of the top 5 papers downloaded from AAPSJ in the second half of 2014).http://rd.springer.com/article/10.1208%2Fs12248-014-9640-5
Li, L., I. Gardner, R. Rose, and M. Jamei, Incorporating Target Shedding Into a Minimal PBPK-TMDD Model for Monoclonal Antibodies, CPT: pharmacomet syst pharmacol, 3:e96, 2014.http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3910015/
Dostalek, M., I. Gardner, B. M. Gurbaxani, R. H. Rose, and M. Chetty, Pharmacokinetics, Pharmacodynamics and Physiologically-Based Pharmacokinetic Modelling of Monoclonal Antibodies, Clin Pharmacokinet, 52:83-124, 2013. http://rd.springer.com/article/10.1007%2Fs40262-012-0027-4
*This post received editorial support by Suzanne Minton.
To learn more about how PBPK modeling is used to support drug development, please watch a webinar my colleague, Dr. Karen Rowland Yeo, gave on this topic.