Pivotal Dose of Pembrolizumab: A Dose-Finding Strategy for Immuno-Oncology

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development

Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Utility and impact of quantitative pharmacology on dose selection and clinical development of immuno-oncology therapy

Immuno-oncology (IO) therapies have changed the cancer treatment landscape. Immune checkpoint inhibitors (ICIs) have improved overall survival in 20-40% of patients with malignancies that were previously refractory. Due to the uniqueness in biology, modalities and patient responses, drug development strategies for IO differed from that traditionally used for cytotoxic and target therapies in oncology, and quantitative pharmacology utilizing modeling approach can be applied in all phases of the development process. In this review, we used case studies to showcase how various modeling methodologies were applied from translational science and dose selection through to label change, using examples that included anti-programmed-death-1 (anti-PD-1), anti-programmed-death ligand-1 (anti-PD-L1), anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4), and anti-glucocorticoid-induced tumor necrosis factor receptor-related protein (anti-GITR) antibodies. How these approaches were utilized to support phase I-III dose selection, the design of phase III trials, and regulatory decisions on label change are discussed to illustrate development strategies. Model-based quantitative approaches have positively impacted IO drug development, and a better understanding of the biology and exposure-response relationship may benefit the development and optimization of new IO therapies.

PD-1 and PD-L1: architects of immune symphony and immunotherapy breakthroughs in cancer treatment

PD-1 (Programmed Cell Death Protein-1) and PD-L1 (Programmed Cell Death Ligand-1) play a crucial role in regulating the immune system and preventing autoimmunity. Cancer cells can manipulate this system, allowing them to escape immune detection and promote tumor growth. Therapies targeting the PD-1/PD-L1 pathway have transformed cancer treatment and have demonstrated significant effectiveness against various cancer types. This study delves into the structure and signaling dynamics of PD-1 and its ligands PD-L1/PD-L2, the diverse PD-1/PD-L1 inhibitors and their efficacy, and the resistance observed in some patients. Furthermore, this study explored the challenges associated with the PD-1/PD-L1 inhibitor treatment approach. Recent advancements in the combination of immunotherapy with chemotherapy, radiation, and surgical procedures to enhance patient outcomes have also been highlighted. Overall, this study offers an in-depth overview of the significance of PD-1/PD-L1 in cancer immunotherapy and its future implications in oncology.

What is the optimal duration, dose and frequency for anti-PD1 therapy of non-small cell lung cancer?

Over the past decade, immune checkpoint inhibitors (ICIs) have transformed the management of multiple malignancies including lung cancer. However, the optimal use of these agents in terms of duration, dose and administration frequency remains unknown. Focusing on anti-PD1 agents nivolumab and pembrolizumab in the context of non-small cell lung cancer, we argue that several lines of evidence suggest current administration regimens of these drugs may result in overtreatment with potentially important implications for cost, quality of life and toxicity. This review summarizes evidence for the scope to optimize anti-PD1 regimens, the limitations of existing data and potential approaches to solve these problems including with a novel multi-arm clinical trial design implemented in the recently opened REFINE-Lung study.

Sabatolimab (MBG453) model-informed drug development for dose selection in patients with myelodysplastic syndrome/acute myeloid leukemia and solid tumors

Sabatolimab is a novel immunotherapy with immuno-myeloid activity that targets T-cell immunoglobulin domain and mucin domain-3 (TIM-3) on immune cells and leukemic blasts. It is being evaluated for the treatment of myeloid malignancies in the STIMULUS clinical trial program. The objective of this analysis was to support the sabatolimab dose-regimen selection in hematologic malignancies. A population pharmacokinetic (PopPK) model was fit to patients with solid tumors and hematologic malignancies, which included acute myeloid leukemia, myelodysplastic syndrome (including intermediate-, high-, and very high-risk per Revised International Prognostic Scoring System), and chronic myelomonocytic leukemia. The PopPK model, together with a predictive model of sabatolimab distribution to the bone marrow and binding to TIM-3 was used to predict membrane-bound TIM-3 bone marrow occupancy. In addition, the total soluble TIM-3 (sTIM-3) kinetics and the pharmacokinetic (PK) exposure-response relationship in patients with hematologic malignancies were examined. At intravenous doses above 240 mg Q2w and 800 mg Q4w, we observed linear PK, a plateau in the accumulation of total sTIM-3, and a flat exposure-response relationship for both safety and efficacy. In addition, the model predicted membrane-bound TIM-3 occupancy in the bone marrow was above 95% in over 95% of patients. Therefore, these results support the selection of the 400 mg Q2w and 800 mg Q4w dosing regimens for the STIMULUS clinical trial program.

Advances in pharmacokinetics and pharmacodynamics of PD-1/PD-L1 inhibitors

Immune checkpoint inhibitors (ICIs) are a group of drugs designed to improve the therapeutic effects on various types of malignant tumors. Irrespective of monotherapy or combinational therapies as first-line and later-line therapy, ICIs have achieved benefits for various tumors. Programmed cell death protein-1 (PD-1) / ligand 1 (PD-L1) is an immune checkpoint that suppresses antitumor immunity, especially in the tumor microenvironment (TME). PD-1/PD-L1 immune checkpoint inhibitors block tumor-related downregulation of the immune system, thereby enhancing antitumor immunity. In comparison with traditional small-molecule drugs, ICIs exhibit pharmacokinetic characteristics owing to their high molecular weight. Furthermore, different types of ICIs exhibit different pharmacodynamic characteristics. Hence, ICIs have been approved for different indications by the Food and Drug Administration (FDA) and National Medical Products Administration (NMPA). This review summarizes pharmacokinetic and pharmacodynamic studies of PD-1/ PD-L1 inhibitors to provide a reference for rational clinical application.

Dose Finding in Oncology: What is Impeding Coming of Age?

After a drug molecule enters clinical trials, there are primarily three levers to enhance probability of success: patient selection, dose selection and choice of combination agents. Of these, dose selection remains an under-appreciated aspect in oncology drug development despite numerous peer-reviewed publications. Here, we share practical challenges faced by the biopharmaceutical industry that reduce the willingness to invest in dose finding for oncology drugs. First, randomized dose finding admittedly slows down clinical development. To reduce the size of dose finding study, trend in exposure vs. tumor-size analysis can be assessed, instead of a statistical test for non-inferiority between multiple doses. Second, investment in testing a lower dose when benefit-risk at the higher dose is sufficient for regulatory approval (i.e., efficacy at the higher dose is better than standard of care and safety is acceptable) is perceived as low priority. Changing regulatory landscape must be considered to optimize dose in pre-marketing setting as post-marketing changes in dose can be commercially costly. Third, the risk of exposing patients to subtherapeutic exposures with a lower dose should be assessed scientifically instead of assuming a monotonic relationship between dose and efficacy. Only the doses which are expected to be at the plateau of dose/exposure-response curve should be investigated in Phase 1b/2. Overall, changing the perceptions that have been impeding investment in dose finding in oncology requires pragmatic discourse among biopharmaceutical industry, regulatory agencies and academia. These perceptions should also not deter dose finding for recently emerging modalities, including BITEs and CART cell therapies.

Predictive Simulations in Preclinical Oncology to Guide the Translation of Biologics

Preclinical in vivo studies form the cornerstone of drug development and translation, bridging in vitro experiments with first-in-human trials. However, despite the utility of animal models, translation from the bench to bedside remains difficult, particularly for biologics and agents with unique mechanisms of action. The limitations of these animal models may advance agents that are ineffective in the clinic, or worse, screen out compounds that would be successful drugs. One reason for such failure is that animal models often allow clinically intolerable doses, which can undermine translation from otherwise promising efficacy studies. Other times, tolerability makes it challenging to identify the necessary dose range for clinical testing. With the ability to predict pharmacokinetic and pharmacodynamic responses, mechanistic simulations can help advance candidates from in vitro to in vivo and clinical studies. Here, we use basic insights into drug disposition to analyze the dosing of antibody drug conjugates (ADC) and checkpoint inhibitor dosing (PD-1 and PD-L1) in the clinic. The results demonstrate how simulations can identify the most promising clinical compounds rather than the most effective in vitro and preclinical in vivo agents. Likewise, the importance of quantifying absolute target expression and antibody internalization is critical to accurately scale dosing. These predictive models are capable of simulating clinical scenarios and providing results that can be validated and updated along the entire development pipeline starting in drug discovery. Combined with experimental approaches, simulations can guide the selection of compounds at early stages that are predicted to have the highest efficacy in the clinic.

First-in-human phase 1 study of the anti-TIGIT antibody vibostolimab as monotherapy or with pembrolizumab for advanced solid tumors, including non-small cell lung cancer

BACKGROUND

In this first-in-human phase 1 study, we investigated the safety and efficacy of the anti-TIGIT antibody vibostolimab as monotherapy or in combination with pembrolizumab.

METHODS

Part A enrolled patients with advanced solid tumors and part B enrolled patients with non-small cell lung cancer (NSCLC). Patients received vibostolimab 2.1-700 mg alone or with pembrolizumab 200 mg in part A and vibostolimab 200 mg alone or with pembrolizumab 200 mg in part B. Primary end points were safety and tolerability. Secondary end points included pharmacokinetics and objective response rate (ORR) per RECIST v1.1.

RESULTS

Part A enrolled 76 patients (monotherapy, 34; combination therapy, 42). No dose-limiting toxicities were reported. Across doses, 56% of patients receiving monotherapy and 62% receiving combination therapy had treatment-related adverse events (TRAEs); grade 3-4 TRAEs occurred in 9% and 17% of patients, respectively. The most common TRAEs were fatigue (15%) and pruritus (15%) with monotherapy and pruritus (17%) and rash (14%) with combination therapy. Confirmed ORR was 0% with monotherapy and 7% with combination therapy. In part B, 39 patients had anti-PD-1/PD-L1-naïve NSCLC (all received combination therapy) and 67 had anti-PD-1/PD-L1-refractory NSCLC (monotherapy, 34; combination therapy, 33). In patients with anti-PD-1/PD-L1-naive NSCLC: 85% had TRAEs-the most common were pruritus (38%) and hypoalbuminemia (31%); confirmed ORR was 26%, with responses occurring in both PD-L1-positive and PD-L1-negative tumors. In patients with anti-PD-1/PD-L1‒refractory NSCLC: 56% receiving monotherapy and 70% receiving combination therapy had TRAEs-the most common were rash and fatigue (21% each) with monotherapy and pruritus (36%) and fatigue (24%) with combination therapy; confirmed ORR was 3% with monotherapy and 3% with combination therapy.

CONCLUSION

Vibostolimab plus pembrolizumab was well tolerated and demonstrated antitumor activity in patients with advanced solid tumors, including patients with advanced NSCLC.

Modeling Pharmacokinetics and Pharmacodynamics of Therapeutic Antibodies: Progress, Challenges, and Future Directions

With more than 90 approved drugs by 2020, therapeutic antibodies have played a central role in shifting the treatment landscape of many diseases, including autoimmune disorders and cancers. While showing many therapeutic advantages such as long half-life and highly selective actions, therapeutic antibodies still face many outstanding issues associated with their pharmacokinetics (PK) and pharmacodynamics (PD), including high variabilities, low tissue distributions, poorly-defined PK/PD characteristics for novel antibody formats, and high rates of treatment resistance. We have witnessed many successful cases applying PK/PD modeling to answer critical questions in therapeutic antibodies’ development and regulations. These models have yielded substantial insights into antibody PK/PD properties. This review summarized the progress, challenges, and future directions in modeling antibody PK/PD and highlighted the potential of applying mechanistic models addressing the development questions.