Optimize Immuno-oncology Drug Discovery and Development Using Quantitative Systems Pharmacology

Optimize Immuno-oncology Drug Discovery and Development Using Quantitative Systems Pharmacology

Immuno-oncology – The Breakthrough in Cancer Therapeutics

Cancer immuno-oncology (IO) uses the body’s natural defenses to combat cancer. These therapies stimulate an individual’s immune system and restore its ability to identify and destroy cancer cells. Anti-cancer immune responses are often inhibited during the spread of cancer. Ultimately, IO therapy expedites long term responses against cancer by contributing long-lasting memory to the immune system.

Since the 2014 breakthrough approvals for the treatment of advanced melanoma with the IO drugs pembrolizumab (Keytruda®) and nivolumab (Opdivo®), the IO drug market has transformed the oncology therapeutics landscape. These and subsequent IO therapies have delivered long-lasting anti-cancer benefits to patients who previously had few options.

About five years have passed since the introduction of checkpoint inhibitors Keytruda and Opdivo, a fact highlighted at the 2019 American Society of Clinical Oncology (ASCO) conference. Data shared at ASCO showed that nearly one fifth of advanced lung cancer patients treated with Keytruda in an early study of the therapy are alive today, a survival rate quadruple that prior to its introduction. A combination of Opdivo and Yervoy® also significantly improved survival rates in previously treated or untreated metastatic melanoma.

 

Advances in Cancer Therapeutics Using Checkpoint Inhibitors

There are nearly 3,400 IO therapies in the current global drug development pipeline with 1,300 in clinical studies. Immune checkpoint inhibitors have emerged as a novel IO therapy option for certain cancers. As described by the National Cancer Institute (NCI), “checkpoint inhibitors help keep immune responses in check and prevent T cells from killing cancer cells.” According to the NCI, when these proteins are blocked, the “brakes on the immune system are released and T cells are able to kill cancer cells better”.

Checkpoint inhibitors, which include programmed cell death-1 (PD-1), PD-ligand 1 (PD-L1), and cytotoxic T-lymphocyte–associated antigen 4 (CTLA-4) inhibitors, have demonstrated clinical efficacy for a variety of cancers including non–small cell lung cancer, melanoma, urothelial cancer, Hodgkin’s and non-Hodgkin’s lymphoma, head/neck cancer, subsets of colon and breast cancers, and certain solid tumors.1

Checkpoint inhibitors continue to demonstrate extraordinary clinical profiles and extended indications. The development of PD-1 and CTLA-4 inhibitors and other IO agents as monotherapies have advanced cancer treatment. However, while complete regression and higher long-term survival rates is achieved in some patients, only a subset of patients exhibit durable responses.2

 

Developing More Efficacious Combination IO Therapies

Combination therapies using checkpoint inhibitors have been shown to be a viable approach to developing IO therapies with higher responses. While combination therapies are successfully being leveraged, they can also cause higher toxicities. Developing more efficacious checkpoint inhibitor therapies require a better approach to patient selection through simplified biomarker development and other factors, comprehension of the disease pathophysiology, and optimized clinical trial design. A better comprehension of the multifaceted interaction between a tumor and the immune system will lead to the development of more efficacious treatments.

Second generation IO therapy development focuses on IO therapies that can be synergistically combined with other immunotherapies, or non-IO strategies and emphasizes immunotherapy personalization.3 Examples include targeted therapies, co-stimulatory mAbs, bifunctional agents, epigenetic modulators, vaccines, nanoparticles, adoptive T-cell therapy, oncolytic viruses, and synthetic gene circuits.

 

The Challenge of Combination IO Therapy Drug Development

Due to the number of possible drug combinations, coupled with the
complex biological and pathological processes involved in IO, developing effective IO combination therapies, particularly in refractory patients, is daunting, complex, and difficult.

Developing successful combinations – involving different modalities and diverse biological pathways – cannot be done randomly. It requires knowledge-based guidance. Further, because the potential number and types of IO combinations cannot possibly be tested clinically, simulation using mechanistic models representing current knowledge is a viable method for combination analysis.

 

Using a Quantitative Systems Pharmacology Approach to Advance Combination IO Therapy

A Quantitative Systems Pharmacology (QSP)  approach for developing combination IO therapies can be used to better predict effective drug combinations. QSP can help correlate the physiological differences between preclinical models and human patients. This approach combines computational modeling and experimental data to examine the relationships between a drug, the biological system, and the disease process. QSP models are built using human physiology and pathology and provide an in silico framework for constructing mechanistic, mathematical models of drug action. QSP focuses on the area between pharmacokinetics/pharmacodynamics (PK/PD) and systems biology. QSP translates PK or exposure into pharmacological effect and builds on insights gained from pharmacometric, PK/PD, and physiologically-based PK (PBPK) approaches with systems biology models of biological and biochemical processes.

QSP is recognized by sponsors and global regulatory agencies as a valuable scientific approach to increase understanding of disease biology, improve target selection, and help to ensure drug safety and efficacy in clinical trials. QSP can also be used to improve the design of First-in-Human (FIH) clinical trials that determine the starting dose and subsequent dose escalations to ensure the best possible protection for human subjects.4

QSP is distinct from other Model-informed Drug Development (MIDD) approaches, such as pharmacometrics, since it helps to fill the gaps between the early-stage PK and late-stage drug efficacy using a mechanistic approach. The key to successfully developing IO therapies will be selecting optimal combination therapies and dosing regimens tailored to specific cancers and patient populations. The development of QSP models of interactions between tumor, the immune system, and therapies, combined with the use of The Virtual Twin® technology, will be required for rational development decisions and the regulatory approval process.

 

Creating an IO QSP Consortium to Tackle IO Combination Drug Development

Certara formed a QSP IO Consortium in 2018 that brings together leading biopharmaceutical companies in a pre-competitive environment to cooperatively develop a robust Immuno-oncology Simulator based on state-of-the art QSP science and methods. The IO Simulator will be used to predict optimal combinations, dose regimens, and biomarkers in computer-generated diverse virtual patient populations. By capturing the complexity of biology, the QSP IO Simulator will enable researchers to explore therapeutic combinations with a virtual population, including drugs that use different modalities. It will help sponsors to answer “what if” questions by providing input and guidance for clinical development. The development of QSP models of interactions between tumor, the immune system, and therapies will be the requirement for rational drug development decisions and facilitating the regulatory approval process.

For a comprehensive overview of how QSP can help manage immunogenicity please read our blog on this topic.

References

  1. Moore, C.D., and Chen, I. (2018). Immunotherapy in cancer treatment: A review of checkpoint inhibitors. S. Pharmacist, 43(2), 27-31.
  2. Swart, M, Verbrugger, I., and Beltman, J.B. (2016). Combination approaches with immune-checkpoint blockade in cancer therapy. Frontiers in Oncology, 6, 1-16.
  3. Marshall, H.T., and Djamgoz, M.B.A. (2018). Immuno-oncology: Emerging targets and combination therapies. Frontiers in Oncology 8(315), 1-29.
  4. Van der Graaf PH & Benson N. (2018). The role of quantitative systems pharmacology in the design of first-in-human trials. Clinical Pharmacology & Therapeutics, Epub ahead of
Piet van der Graaf and Andrzej Kierzek

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Piet van der Graaf and Andrzej Kierzek

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Andrzej Kierzek graduated with an undergraduate degree in molecular biology from the University of Warsaw and received a PhD in biophysics from Polish Academy of Sciences in 1999. Since 2004, he has been working at University of Surrey, UK and became Professor of Systems Biology in 2011. In April 2016, he moved to Certara QSP as Head of Systems Modeling. He is still a visiting Professor of Systems Biology at Surrey. Andrzej has more than 20 years of experience in computational biology. He has been working in computational systems biology for over 15 years. He published models and software for analysis of molecular network dynamics and constraint-based modeling of genome scale metabolic networks, including metabolic reprogramming in cancer. Currently, his research focus is on immune-oncology and immunogenicity. Piet has 15 years’ experience working in the pharmaceutical industry at Synthélabo (Sanofi) and Pfizer, where he held leadership positions in Discovery Biology, Pharmacokinetics & Drug Metabolism (PDM) and Clinical Pharmacology/Pharmacometrics. He obtained his PharmD from the University of Groningen, The Netherlands, and a PhD in quantitative pharmacology from King’s College London, UK, and was elected as a Fellow of the British Pharmacological Society. He is also Editor-in-Chief of CPT: Pharmacometrics & Systems Pharmacology. He brings his considerable skill and experience to our projects and contributes to the strategic development of Certara.