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5 Points to Consider When Designing an Immuno-oncology Clinical Pharmacology Program

Immunotherapy presents interesting and proximally viable therapeutic options in the growing armamentarium of treatment modalities to combat complex, multifactorial diseases. Indeed, its revolution within oncology is astounding. The field has witnessed rampant and clinically significant advances, with the approval of several checkpoint inhibitors. Clinical trials with these inhibitors suggest that there are more robust and durable responses to managing many tumor types. The focus has gainfully shifted to combination therapies as efforts are driving towards increasing overall survival. The field of combination immunotherapy, however, has presented challenges to the drug developer. Nevertheless, the immuno-oncology toolkit continues to present remarkable opportunities for the development of new therapeutics. In this blog, I reflect on 5 key development considerations when designing such a program.

Development consideration #1: selecting the right dose

Due to the uniqueness of oncology as a therapeutic area, dose exploration has traditionally been approached from the point of view of dose-limiting tolerability (DLT). Perhaps this is a carryover from the development of cytotoxic agents which were fraught with safety considerations. Because of the DLT focused mindset, oncologists have focused on identifying the maximum tolerated dose (MTD). In most cases, the MTD is used as the recommended phase 2 dose (RP2D). While such an approach might have been meaningful for cytotoxic agents, such a carte blanche dose burden approach could yield unintended consequences for newer, precision-based medicine approaches, where specific target mediated enzyme or receptor modulation is anticipated. Newer agents would benefit from a more model-informed dose exploration strategy to identify the minimum effective dose and the pharmacological dose range. In fact, I would argue that minimum effective doses have more scientific relevance than MTD for precision targets. Because oncology first-in-human trials are generally smaller than first-in-human trials in the non-oncology space, the probability of type 2 error rates are considerable. For this reason, the traditional emphasis on 3+3 designs should give way towards more model-informed adaptive study designs where the goal is to understand the dose dynamic range (a.k.a. therapeutic range) and more specifically, the minimum effective dose [1].

Development consideration #2: selecting the right patient responsive population

Some patients receiving immunotherapeutics don’t respond favorably to treatment. This has led the way to understand tumor responsiveness through the use of molecular diagnostics. Such approaches have resulted in elevation of predictive biomarkers of tumor response and to the development and approval of companion diagnostics [2]. The joint approval of a drug and diagnostic presents a unique advantage for drug developers. The first drug-diagnostic combination product approved by the FDA was trastuzumab paired with HercepTest™, which measured HER2/ERBB2 expression in breast cancer tissue. The first examples of FDA-approved companion diagnostics for checkpoint blockers include the Dako PD-L1 IHC 22C3 PharmDx assay and the Ventana PD-L1 (SP142) assay for determining PD-L1 expression, before beginning treatment with pembrolizumab or atezolizumab, respectively [2]. More recently, the FDA approved the PD-L1 IHC 28-8 PharmDx before starting treatment with nivolumab and ipilimumab [2]. These assays can identify subsets of a patient population that will benefit the most from treatment. A growing list of companion diagnostics approved by the FDA can be found here. It is crucial for drug developers to understand patient response enrichment and to decide whether a companion diagnostic is a prudent option.

Development consideration #3: understanding mechanism of action and complementarity for combinations

A key goal of tumor immunology has been to leverage the knowledge of how innate and adaptive immune systems work. A fundamental premise has been that T cell responses to select tumor types are inhibited by CTLA4 and PD1, thus promoting therapeutics targeting the blockade of CTLA4 and PD1/PDL1 pathways. Clinical trials studying combinations of CTLA4 and PD1 have also shown promise. There are also efforts towards tumor vaccines using TLR ligands, dendritic cellular vaccines, or DNA vaccines and viral vectors. However, little is known about the autoimmune burden of these combinations, and what roles, if any, do the combinations have on detrimental inflammatory pathways. Other combinations include combining tumor vaccines with checkpoint blockade or checkpoint blockade with other modalities, including but not limited to chemoembolization, chemotherapy, radiation therapy, or passive immunotherapy with adoptive cell based therapies. A cursory search in clinicaltrials.gov reveals a plethora of immunotherapy combinations for oncology.

When designing a combination program, it is essential to know how each component of the combination acts alone and in combination, either additively or synergistically. Understanding this aspect ensures that the drugs are tested in the right sequence and whether a spacing interval needs to be taken into consideration.

Development consideration #4: selecting the right endpoints

The regulatory endpoints for understanding clinical trial successes are precedented endpoints including objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). OS is the gold standard endpoint. Because of early immunomodulation response observed with immunotherapy and the somewhat delayed therapeutic immune response, ORR (proportion of patients yielding a complete or partial response) has been traditionally employed as a meaningful endpoint in immunotherapeutic development. ORR, based on imaging criteria, has limitations, in that it does not require comparison with a control arm. ORR may also not be amenable to meaningful exposure/response relationships and could induce bias in those assessments. Moreover, the ORR and OS may not be correlated. Challenges in applying Response Evaluation Criteria in Solid Tumors (RECIST) and its evolving variations (immune related) have highlighted the problems in assessing tumor burden as it relates to new lesions as well as issues relating to confirmation of pharmacodynamics (PD).

Because early phase trials serve as learning trials, it is imperative to ensure optimal enrichment of PD biomarkers to understand the mechanism of immunotherapeutic action. Such approaches, coupled with PK sampling, could provide mechanistic insights and enable trial simulations of outcomes, vis-à-vis clinical benefit. These biomarkers could include assessment of T-cell responses, gene signatures, and/or cytokines [3].

Development consideration #5: seamless phase 1/3 program incorporating MIDD principles

Clinical trials are stage gated, phased trials with an artificial distinction between phase 1, 2 and 3 clinical trials. This may be symptomatic of how organizations are structured and how responsibilities are assigned, and also tolerance towards financial risks within smaller enterprises [4]. Such an artificial divide between trials creates loss of opportunities to ensure continuous within subject learnings on disease progression and treatment response. The advantage of a seamless phase 1/3 program is in pursuit of safe dose exploration for monotherapies and combinations. Either dose or endpoint adaptations within the trial can improve the learning parts of the trial and leverage that for the more confirmatory parts of the trial. Such a seamless trial, augmented by sound MIDD and statistical frameworks [5] as well as biomarker enrichment [6], offers the benefit of not compartmentalizing patient demographics or tumor types, while using adaptation to assess the utility of schedule optimization, endpoints, and a quicker timeline to product approval. This ultimately will reduce uncertainty in drug development and further bring rational development to the forefront.

For more information on Certara capabilities within immuno-oncology, please watch this webinar:


References

  1. Krishna R, Bolognese JA. Novel clinical trial designs in clinical pharmacology and experimental medicine (book chapter). In Dose Optimization in Drug Development (Rajesh Krishna – Editor), Drugs and Pharmaceutical Sciences Series, Taylor and Francis, New York, 2006.
  2. Jørgensen JT, Nielsen KB. Companion and complementary diagnostics for first-line immune checkpoint inhibitor treatment in non-small cell lung cancer. Transl Lung Cancer Res2018;7(Suppl 2):S95-S99.
  3. Naidus E, et al. Early changes in the circulating T cells are associated with clinical outcomes after PD-L1 blockade by durvalumab in advanced NSCLC patients. Cancer Immunol Immunother 2021 Jan 9. doi: 10.1007/s00262-020-02833-z.
  4. Krishna R. Enabling warp speed using the hypervelocity innovation model: a blue print for drug development in pandemics. Clin Transl Sci 13(6):1014-1018.
  5. Chelliah V et al. Quantitative systems pharmacology approaches for immunooncology: adding virtual patients to the development paradigm. Clin Pharmacol Ther 109 (3), 605-618, 2021.
  6. Littman B, Krishna R (Editors). Translational medicine and drug discovery, Cambridge University Press, UK, January 2011 (ISBN: 13: 9780521886451).

About the author

By: Rajesh Krishna

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