Optimal Dosing for Targeted Therapies in Oncology: Drug Development Cases Leading by Example

Dosage Optimization: A Regulatory Perspective for Developing Oncology Drugs

Optimized dosages provide a secure foundation for the development of well-tolerated and effective oncology drugs. Project Optimus, an initiative within the Oncology Center of Excellence, strives to reform the dosage optimization and dosage selection paradigm in oncology. This initiative stems from the availability of targeted drugs and from the demand for more tolerable dosages from patients, caregivers, and advocates. Reforming dosage optimization for oncology drugs requires a paradigm shift from the one employed for cytotoxic chemotherapy to one that understands and considers the unique attributes of targeted therapy, immunotherapy, and cellular therapy. Limited characterization of dosage during drug development may result in (1) patients not staying on a therapy long-term due to poor tolerability, (2) failure to establish positive benefit-risk in clinical trials for regulatory approval (3) withdrawal of drugs from the market (4) removal of indications of drugs from the market. Timely access to drugs is important for all patients with cancer, so it is vital to iteratively analyze all nonclinical and clinically relevant data at each stage of development and leverage quantitative approaches, innovative trial designs, and regulatory support to arrive at dosages with favorable benefit-risk. This review highlights the key challenges and opportunities to embracing this new paradigm and realizing the full potential of new oncology therapies.

Design and sample size determination for multiple-dose randomized phase II trials for dose optimization

The U.S. Food and Drug Administration (FDA) has launched Project Optimus to shift dose selection from the maximum tolerated dose (MTD) to the dose that produces the optimal risk-benefit tradeoff. One approach highlighted in the FDA's guidance involves conducting a randomized phase II trial following the completion of a phase I trial, where multiple doses (typically including the MTD and one or two doses lower than the MTD) are compared to identify the optimal dose that maximizes the benefit-risk tradeoff. This article focuses on the design of such a multiple-dose randomized trial, specifically the determination of the sample size. We generalized the standard definitions of type I error and power to accommodate the unique characteristics of dose optimization and derived a decision rule along with an algorithm to determine the optimal sample size. The resulting design is referred to as MERIT (Multiple-dosE RandomIzed Trial design for dose optimization based on toxicity and efficacy). Simulation studies demonstrate that MERIT has desirable operating characteristics, and a sample size between 20 and 40 per dosage arm often offers reasonable power and type I errors to ensure patient safety and benefit. To facilitate the implementation of the MERIT design, we provide software, available at https://www.trialdesign.org.

Implementing and assessing Bayesian response-adaptive randomisation for backfilling in dose-finding trials

Traditional approaches in dose-finding trials, such as the continual reassessment method, focus on identifying the maximum tolerated dose. In contemporary early-phase dose-finding trials, especially in oncology with targeted agents or immunotherapy, a more relevant aim is to identify the lowest dose level that maximises efficacy whilst remaining tolerable. Backfilling, defined as the practice of assigning patients to dose levels lower than the current highest tolerated dose, has been proposed to gather additional pharmacokinetic, pharmacodynamic and biomarker data to recommend the most appropriate dose to carry forward for subsequent studies. The first formal framework [5] for backfilling proposed randomising backfill patients with equal probability among those doses below the dose level where the study is currently at. Here, we propose to use Bayesian response-adaptive randomisation to backfill patients. This patient-oriented approach to backfilling aims to allocate more patients to dose levels in the backfill set with higher expected efficacy based on emerging data. The backfill set constitutes of the doses below the dose the dose-finding algorithm is at. At study completion, collective patient data inform the dose-response curve, suggesting an optimal dose level balancing toxicity and efficacy. Our simulation study across diverse clinical trial settings demonstrates that a backfilling strategy using Bayesian response-adaptive randomisation can result in a patient-oriented patient assignment procedure which simultaneously enhances the likelihood of correctly identifying the most appropriate dose level. This contribution offers a methodological framework and practical implementation for patient-oriented backfilling, encompassing design and analysis considerations in early-phase trials.

Informing the Recommended Phase III Dose of Alnuctamab, a CD3 × BCMA T-Cell Engager, Using Population Pharmacokinetics and Exposure-Response Analysis

Alnuctamab, a B-cell maturation antigen (BCMA)-targeting T-cell engager, has demonstrated encouraging antitumor activity in the phase I study CC-93269-MM-001 treating patients with relapsed or refractory multiple myeloma. Identification of a recommended Phase III dose (RP3D) was a key objective, as such population pharmacokinetic (PopPK) and exposure-response analysis was critical. Intravenous (IV) alnuctamab was administered in fixed doses (0.15-10 mg) or in step-up doses to a maximum 10-mg target dose. Subcutaneous (SC) step-up doses of 3 and 6 mg were followed by a target dose range of 10-60 mg. Concentration data from IV and SC alnuctamab administration was pooled and was well described by a two-compartment PopPK model with first-order absorption and elimination. Covariate analysis determined that the inclusion of baseline soluble BCMA (sBCMA) on clearance significantly improved model fitting. Individual exposure parameters were estimated from the final model to characterize exposure-response relationships. Switching from IV to SC administration improved the safety profile of alnuctamab by limiting the frequency of grade ≥2 CRS events. A significant exposure-CRS relationship was observed after the first SC dose, but not subsequent dose administrations. Exposure-safety analysis did not find a statistically significant relationship between increasing exposure and the probability of key safety events of interest. Logistic regression analysis for patients administered SC alnuctamab identified that increased exposure significantly increased the probability of response, although the additional benefit was minimal at exposures above 30 mg target dose. Considering the totality of exposure-response data, the clinical pharmacology assessment supported a SC RP3D of 3/6/30 mg.

Reduced frequency dosing of osimertinib in EGFR-mutant non-small cell lung carcinoma: real world data

Introduction

Osimertinib is more efficacious and as safe as first-generation epidermal growth factor receptor (EGFR)-directed tyrosine kinase inhibitors. However, osimertinib is not affordable for most patients in developing nations. Moreover, the minimum biologically effective dose of osimertinib may be less than the approved dose.

Materials and methods

This was a retrospective observational multicentric study aimed to describe the efficacy (objective response rate (ORR), disease control rate (DCR), progression free survival (PFS), overall survival (OS)) and toxicity of osimertinib 80 mg orally administered less frequently than daily (ranging from every other day to once-a-week) in patients with EGFR-mutated non-small cell lung cancer.

Results

Between January 2021 and August 2023, we enrolled 22 patients. Six received osimertinib 80 mg once-a-week, nine received 80 mg once-in-3-days and seven received 80 mg on alternate days. Responses included 0 complete responses, 7 (31.8%) partial responses, 9 (40.9%) stable disease and 5 (22.7%) progressive disease. ORR was 31.8%, and DCR was 72.7%. Median PFS was 9.2 months (95% confidence interval (CI) 2.9-15.7), and median OS was 17.8 months (95% CI, 3.2-32.6). In patients who received reduced frequency osimertinib in the second line and beyond, the ORR was 29.4%, DCR was 70.5%, median PFS was 5.9 months (95% CI, 1.1-10.6) and median OS was 17.6 months (95% CI, 2.9-32.2). Grade 3 and higher toxicities were noted in 8 (36.3%) patients.

Conclusion

Less frequent dosing of osimertinib may be a valid treatment option, especially in the second line and beyond setting in patients who cannot afford full dose daily osimertinib. This may provide an additional treatment option with a similar toxicity profile as that of standard dose osimertinib.

To modulate or to skip: De-escalating PARP inhibitor maintenance therapy in ovarian cancer using adaptive therapy

Toxicity and emerging drug resistance pose important challenges in poly-adenosine ribose polymerase inhibitor (PARPi) maintenance therapy of ovarian cancer. We propose that adaptive therapy, which dynamically reduces treatment based on the tumor dynamics, might alleviate both issues. Utilizing in vitro time-lapse microscopy and stepwise model selection, we calibrate and validate a differential equation mathematical model, which we leverage to test different plausible adaptive treatment schedules. Our model indicates that adjusting the dosage, rather than skipping treatments, is more effective at reducing drug use while maintaining efficacy due to a delay in cell kill and a diminishing dose-response relationship. In vivo pilot experiments confirm this conclusion. Although our focus is toxicity mitigation, reducing drug use may also delay resistance. This study enhances our understanding of PARPi treatment scheduling and illustrates the first steps in developing adaptive therapies for new treatment settings. A record of this paper's transparent peer review process is included in the supplemental information.

Evolving or immutable - phase I solid tumor trials in the era of precision oncology

In the era of precision oncology (PO), systemic therapies for patients (pts) with solid tumors have shifted from chemotherapy (CT) to targeted therapy (TT) and immunotherapy (IO). This systematic survey describes features of trials enrolling between 2010 and 2020, focusing on inclusion criteria, type of dose escalation scheme (DES) utilized, and use of expansion cohorts (ECs). A literature search identified phase I studies in adults with solid tumors published January 1, 2000- December 31, 2020 from 12 journals. We included only studies enrolling between 2010 and 2020 to better capture the PO era. Two reviewers abstracted data; a third established concordance. Of 10,744 studies, 10,195 were non-topical or enrolled prior to 2010; 437 studies were included. The most common drug classes were TT (47.6%), IO (22%), and CT (6.9%). In studies which reported race, patients were predominantly white (61.7%) or Asian (25.7%), followed by black (6.5%) or other (6.1%). Heterogeneity was observed in the reporting and specification of study inclusion criteria. Only 40.1% of studies utilized ECs, and among the studies which used ECS, 46.6% were defined by genomic selection. Rule-based DES were used in 89% of trials; a 3+3 design was used in 80.5%. Of all drugs tested, 37.5% advanced to phase II, while 10.3% garnered regulatory licensure (for an indication tested in phase I). In the era of PO, TT and IO have emerged as the most studied agents in phase I trials. Rule-based DES, which are more relevant for escalating CT, are still chiefly utilized.

Statistical and practical considerations in planning and conduct of dose-optimization trials

The U.S. Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection toward identifying the optimal biological dose that offers the best balance between benefit and risk, rather than the maximum tolerated dose. However, achieving dose optimization is a challenging task that involves a variety of factors and is considerably more complicated than identifying the maximum tolerated dose, both in terms of design and implementation. This article provides a comprehensive review of various design strategies for dose-optimization trials, including phase 1/2 and 2/3 designs, and highlights their respective advantages and disadvantages. In addition, practical considerations for selecting an appropriate design and planning and executing the trial are discussed. The article also presents freely available software tools that can be utilized for designing and implementing dose-optimization trials. The approaches and their implementation are illustrated through real-world examples.

The 3 + 3 design in dose-finding studies with small sample sizes: Pitfalls and possible remedies

In the last few years, numerous novel designs have been proposed to improve the efficiency and accuracy of phase I trials to identify the maximum-tolerated dose (MTD) or the optimal biological dose (OBD) for noncytotoxic agents. However, the conventional 3+3 approach, known for its and poor performance, continues to be an attractive choice for many trials despite these alternative suggestions. The article seeks to underscore the importance of moving beyond the 3+3 design by highlighting a different key element in trial design: the estimation of sample size and its crucial role in predicting toxicity and determining the MTD. We use simulation studies to compare the performance of the most used phase I approaches: 3+3, Continual Reassessment Method (CRM), Keyboard and Bayesian Optimal Interval (BOIN) designs regarding three key operating characteristics: the percentage of correct selection of the true MTD, the average number of patients allocated per dose level, and the average total sample size. The simulation results consistently show that the 3+3 algorithm underperforms in comparison to model-based and model-assisted designs across all scenarios and metrics. The 3+3 method yields significantly lower (up to three times) probabilities in identifying the correct MTD, often selecting doses one or even two levels below the actual MTD. The 3+3 design allocates significantly fewer patients at the true MTD, assigns higher numbers to lower dose levels, and rarely explores doses above the target dose-limiting toxicity (DLT) rate. The overall performance of the 3+3 method is suboptimal, with a high level of unexplained uncertainty and significant implications for accurately determining the MTD. While the primary focus of the article is to demonstrate the limitations of the 3+3 algorithm, the question remains about the preferred alternative approach. The intention is not to definitively recommend one model-based or model-assisted method over others, as their performance can vary based on parameters and model specifications. However, the presented results indicate that the CRM, Keyboard, and BOIN designs consistently outperform the 3+3 and offer improved efficiency and precision in determining the MTD, which is crucial in early-phase clinical trials.

A Review of the Therapeutic Role of Bosutinib in Chronic Myeloid Leukemia

The development of the BCR::ABL1 tyrosine kinase inhibitors (TKIs) has transformed Philadelphia chromosome (Ph)-positive chronic myeloid leukemia (CML) from a fatal disease to an often-indolent illness that, when managed effectively, can restore a life expectancy close to that of the normal population. Bosutinib is a second-generation TKI approved for adults with Ph-positive CML in chronic phase, accelerated phase, or blast phase that is resistant or intolerant to prior therapy, and for newly diagnosed Ph-positive chronic phase CML. This review details the efficacy of bosutinib for the treatment of CML in the first- and second-line settings, as well as in third- and later-line settings for high-risk patients resistant or intolerant to at least 2 TKIs. It also outlines bosutinib studies that provide evidence for dose-optimization strategies that can be used to improve efficacy and effectively manage adverse events. The studies that provide evidence for specific patient populations benefiting particularly from bosutinib dose-optimization strategies are also discussed. The well-established, long-term side-effect profile and the potential to make dose adjustments with bosutinib make it an appropriate treatment option for patients with CML. Bosutinib has demonstrated a positive impact on health-related quality of life and an important role in the long-term treatment of patients with CML.

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.

Evolving or Immutable - Phase I Solid Tumor Trials in the Era of Precision Oncology

Purpose

In the era of precision oncology (PO), systemic therapies for patients (pts) with solid tumors have shifted from chemotherapy (CT) to targeted therapy (TT) and immunotherapy (IO). This systematic survey describes features of trials enrolling between 2010-2020, focusing on inclusion criteria, type of dose escalation scheme (DES) utilized, and use of expansion cohorts (ECs).

Methods

A literature search identified phase I studies in adults with solid tumors published January 1, 2000 - December 31, 2020 from 12 journals. We included only studies enrolling between 2010-2020 to better capture the PO era. Two reviewers abstracted data; a third established concordance.

Results

Of 10,744 studies, 10,195 were non-topical or enrolled prior to 2010; 437 studies were included. The most common drug classes were TT (47.6%), IO (22%), and CT (6.9%). In studies which reported race, patients were predominantly white (61.7%) or Asian (25.7%), followed by black (6.5%) or other (6.1%). Heterogeneity was observed in the reporting and specification of study inclusion criteria. Only 40.1% of studies utilized ECs, and among the studies which used ECS, 46.6% were defined by genomic selection. Rule-based DES were used in 89% of trials; a 3+3 design was used in 80.5%. Of all drugs tested, 37.5% advanced to phase II, while 10.3% garnered regulatory licensure (for an indication tested in phase I).

Conclusion

In the era of PO, TT and IO have emerged as the most studied agents in phase I trials. Rule-based DES, which are more relevant for escalating CT, are still chiefly utilized.

Pediatric Cancer Drug Development: Leveraging Insights in Cancer Biology and the Evolving Regulatory Landscape to Address Challenges and Guide Further Progress

The discovery and development of anticancer drugs for pediatric patients have historically languished when compared to both past and recent activity in drug development for adult patients, notably the dramatic spike of targeted and immune-oncology therapies. The reasons for this difference are multifactorial. Recent changes in the regulatory landscape surrounding pediatric cancer drug development and the understanding that some pediatric cancers are driven by genetic perturbations that also drive disparate adult cancers afford new opportunities. The unique cancer-initiating events and dependencies of many pediatric cancers, however, require additional pediatric-specific strategies. Research efforts to unravel the underlying biology of pediatric cancers, innovative clinical trial designs, model-informed drug development, extrapolation from adult data, addressing the unique considerations in pediatric patients, and use of pediatric appropriate formulations, should all be considered for efficient development and dosage optimization of anticancer drugs for pediatric patients.

Dose modification dynamics of ponatinib in patients with chronic-phase chronic myeloid leukemia (CP-CML) from the PACE and OPTIC trials

Ponatinib, the only approved all known-BCR::ABL1 inhibitor, is a third-generation tyrosine-kinase inhibitor (TKI) designed to inhibit BCR::ABL1 with or without any single resistance mutation, including T315I, and induced robust and durable responses at 45 mg/day in patients with CP-CML resistant to second-generation TKIs in the PACE trial. However, cardiovascular toxicities, including arterial occlusive events (AOEs), have emerged as treatment-related AEs within this class of TKIs. The OPTIC trial evaluated the efficacy and safety of ponatinib using a novel, response-based, dose-reduction strategy in patients with CP-CML whose disease is resistant to ≥2 TKIs or who harbor T315I. To assess the dose-response relationship and the effect on the safety of ponatinib, we examined the outcomes of patients with CP-CML enrolled in PACE and OPTIC who received 45 mg/day of ponatinib. A propensity score analysis was used to evaluate AOEs across both trials. Survival rates and median time to achieve ≤1% BCR::ABL1IS in OPTIC were similar or better than in PACE. The outcomes of patients with T315I mutations were robust in both trials. Patients in OPTIC had a lower exposure-adjusted incidence of AOEs compared with those in PACE. This analysis demonstrates that response-based dosing for ponatinib improves treatment tolerance and mitigates cardiovascular risk.

Spatiotemporal Quantification of HER2-targeting Antibody-Drug Conjugate Bystander Activity and Enhancement of Solid Tumor Penetration

PURPOSE

Antibody-drug conjugate (ADC) has had a transformative effect on the treatment of many solid tumors, yet it remains unclear how ADCs exert bystander activity in the tumor microenvironment.

EXPERIMENTAL DESIGN

Here, we directly visualized and spatiotemporally quantified the intratumor biodistribution and pharmacokinetics of different ADC components by developing dual-labeled fluorescent probes.

RESULTS

Mechanistically, we found that tumor penetration of ADCs is distinctly affected by their ability to breach the binding site barrier (BSB) in perivascular regions of tumor vasculature, and bystander activity of ADC can only partially breach BSB. Furthermore, bystander activity of ADCs can work in synergy with coadministration of their parental antibodies, leading to fully bypassing BSBs and enhancing tumor penetration via a two-step process.

CONCLUSIONS

These promising preclinical data allowed us to initiate a phase I/II clinical study of coadministration of RC48 and trastuzumab in patients with malignant stomach cancer to further evaluate this treatment strategy in humans.

An IQ consortium analysis of starting dose selection for oncology small molecule first-in-patient trials suggests an alternative NOAEL-based method can be safe while reducing time to the recommended phase 2 dose

The first-in-patient (FIP) starting dose for oncology agents should be reasonably safe and provide potential therapeutic benefit to the patient. For late-stage oncology patients, this dose is often based on the ICH S9 guidance, which was developed primarily based on experience with cytotoxic chemotherapeutic agents using the rodent STD10 or non-rodent HNSTD and an appropriate safety factor. With the increase in molecularly targeted chemotherapeutics, it is prudent to re-evaluate how the FIP dose is derived to ensure that the appropriate balance between risk and therapeutic benefit to the patient is achieved. Blinded data on 92 small molecule oncology compounds from 12 pharmaceutical companies who are members of the IQ DruSafe consortium were gathered to investigate if a NOAEL-based starting dose without a safety factor would have been tolerated in the FIP trial and if so, estimating how many dose escalation cohorts could have been reduced. Our analysis suggests that the NOAEL-based alternative starting dose would have been tolerated in most cases evaluated, with an anticipated mean reduction of 2.3 cohorts. Of the 12 cases where the alternative approach resulted in a starting dose that would have exceeded the MTD/RP2D, none of the nonclinical toxicities in these cases were considered irreversible and would be monitorable in all but one instance. Most non-tolerated cases were within two-threefold of the MTD/RP2D, with the clinical AEs considered manageable and mitigated by dose de-escalation. No one method of FIP dose calculation will likely be appropriate for all oncology small molecules and starting dose selection should be performed using a case-by-case approach. However, the NOAEL-based method that does not utilize a safety factor should be considered when appropriate to minimize the number of patients exposed to sub-therapeutic doses of an investigational oncology agent and accelerating development to RP2D.

At the right dose: personalised (N-of-1) dosing for precision oncology

The objective of oncology therapeutics, especially in the age of precision medicine, is to give the right drug(s) to the right patient at the right time. Yet, a major challenge is finding the right dose for each patient. Determining safe and efficacious doses of oncology treatments, especially for novel combination therapies, can be challenging. Moreover, traditionally, dosing cancer drugs is based on giving each patient the same dose (a flat dose) or a dose based on surface area/weight. But patients' ability to tolerate drugs is influenced by additional factors including, but not limited to age, gender, race, comorbidities, organ function, and metabolism. Herein, we present evidence that, in the era of targeted drugs, individualised drug dosing determined by starting at reduced doses and using intrapatient dose escalation can yield safe and effective personalised dosing of novel combinations of approved drugs that have not previously undergone formal phase I trials and can also optimise dosing of tested drug regimens.

Treatment of evolving cancers will require dynamic decision support

Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.

Seamless phase II/III design: a useful strategy to reduce the sample size for dose optimization

BACKGROUND

The traditional more-is-better dose selection paradigm, originally developed for cytotoxic chemotherapeutics, can be problematic when applied to the development of novel molecularly targeted agents. Recognizing this issue, the US Food and Drug Administration initiated Project Optimus to reform the dose optimization and selection paradigm in oncology drug development, emphasizing the need for greater attention to benefit-risk considerations.

METHODS

We identify different types of phase II/III dose-optimization designs, classified according to trial objectives and endpoint types. Through computer simulations, we examine their operating characteristics and discuss the relevant statistical and design considerations for effective dose optimization.

RESULTS

Phase II/III dose-optimization designs are capable of controlling family-wise type I error rates and achieving appropriate statistical power with substantially smaller sample sizes than the conventional approach while also reducing the number of patients who experience toxicity. Depending on the design and scenario, the sample size savings range from 16.6% to 27.3%, with a mean savings of 22.1%.

CONCLUSIONS

Phase II/III dose-optimization designs offer an efficient way to reduce sample sizes for dose optimization and accelerate the development of targeted agents. However, because of interim dose selection, the phase II/III dose-optimization design presents logistical and operational challenges and requires careful planning and implementation to ensure trial integrity.

Effective dose window for containing tumor burden under tolerable level

A maximum-tolerated dose (MTD) reduces the drug-sensitive cell population, though it may result in the competitive release of drug resistance. Alternative treatment strategies such as adaptive therapy (AT) or dose modulation aim to impose competitive stress on drug-resistant cell populations by maintaining a sufficient number of drug-sensitive cells. However, given the heterogeneous treatment response and tolerable tumor burden level of individual patients, determining an effective dose that can fine-tune competitive stress remains challenging. This study presents a mathematical model-driven approach that determines the plausible existence of an effective dose window (EDW) as a range of doses that conserve sufficient sensitive cells while maintaining the tumor volume below a threshold tolerable tumor volume (TTV). We use a mathematical model that explains intratumor cell competition. Analyzing the model, we derive an EDW determined by TTV and the competitive strength. By applying a fixed endpoint optimal control model, we determine the minimal dose to contain cancer at a TTV. As a proof of concept, we study the existence of EDW for a small cohort of melanoma patients by fitting the model to longitudinal tumor response data. We performed identifiability analysis, and for the patients with uniquely identifiable parameters, we deduced patient-specific EDW and minimal dose. The tumor volume for a patient could be theoretically contained at the TTV either using continuous dose or AT strategy with doses belonging to EDW. Further, we conclude that the lower bound of the EDW approximates the minimum effective dose (MED) for containing tumor volume at the TTV.

Complex PK-PD of an engineered IL-15/IL-15Rα-Fc fusion protein in cynomolgus monkeys: QSP modeling of lymphocyte dynamics

XmAb24306 is a lymphoproliferative interleukin (IL)-15/IL-15 receptor α (IL-15Rα) Fc-fusion protein currently under clinical investigation as an immunotherapeutic agent for cancer treatment. XmAb24306 contains mutations in IL-15 that attenuate its affinity to the heterodimeric IL-15 receptor βγ (IL-15R). We observe substantially prolonged pharmacokinetics (PK) (half-life ∼ 2.5 to 4.5 days) in single- and repeat-dose cynomolgus monkey (cyno) studies compared to wild-type IL-15 (half-life ∼ 1 hour), leading to increased exposure and enhanced and durable expansion of NK cells, CD8+ T cells and CD4-CD8- (double negative [DN]) T cells. Drug clearance varied with dose level and time post-dose, and PK exposure decreased upon repeated dosing, which we attribute to increased target-mediated drug disposition (TMDD) resulting from drug-induced lymphocyte expansion (i.e., pharmacodynamic (PD)-enhanced TMDD). We developed a quantitative systems pharmacology (QSP) model to quantify the complex PKPD behaviors due to the interactions of XmAb24306 with multiple cell types (CD8+, CD4+, DN T cells, and NK cells) in the peripheral blood (PB) and lymphoid tissues. The model, which includes nonspecific drug clearance, binding to and TMDD by IL15R differentially expressed on lymphocyte subsets, and resultant lymphocyte margination/migration out of PB, expansion in lymphoid tissues, and redistribution to the blood, successfully describes the systemic PK and lymphocyte kinetics observed in the cyno studies. Results suggest that after 3 doses of every-two-week (Q2W) doses up to 70 days, the relative contributions of each elimination pathway to XmAb24306 clearance are: DN T cells > NK cells > CD8+ T cells > nonspecific clearance > CD4+ T cells. Modeling suggests that observed cellular expansion in blood results from the influx of cells expanded by the drug in lymphoid tissues. The model is used to predict lymphoid tissue expansion and to simulate PK-PD for different dose regimens. Thus, the model provides insight into the mechanisms underlying the observed PK-PD behavior of an engineered cytokine and can serve as a framework for the rapid integration and analysis of data that emerges from ongoing clinical studies in cancer patients as single-agent or given in combination.

Using an animal model to predict the effective human dose for oral multiple sclerosis drugs

The objective of this study was to determine the potential usefulness of an animal model to predict the appropriate dose of newly developed drugs for treating relapsing remitting multiple sclerosis (RRMS). Conversion of the lowest effective dose (LEffD) for mice and rats in the experimental autoimmune encephalomyelitis (EAE) model was used to predict the human effective dose utilizing the body surface area correction factor found in the 2005 US Food and Drug Administration (FDA) Guidance for Industry in selecting safe starting doses for clinical trials. Predictions were also tested by comparison with doses estimated by scaling up the LEffD in the model by the human to animal clearance ratio. Although initial proof-of-concept studies of oral fingolimod tested the efficacy and safety of 1.25 and 5 mg in treating RRMS, the EAE animal model predicted the approved dose of this drug, 0.5 mg daily. This approach would have also provided useful predictions of the approved human oral doses for cladribine, dimethyl fumarate, ozanimod, ponesimod, siponimod, and teriflunomide, drugs developed with more than one supposed mechanism of action. The procedure was not useful for i.v. dosed drugs, including monoclonal antibodies. We maintain that drug development scientists should always examine a simple allometric method to predict the therapeutic effective dose in humans. Then, following clinical studies, we believe that the animal model might be expected to yield useful predictions of other drugs developed to treat the same condition. The methodology may not always be predictive, but the approach is so simple it should be investigated.

Large inherent variability in data derived from highly standardised cell culture experiments

Cancer drug development is hindered by high clinical attrition rates, which are blamed on weak predictive power by preclinical models and limited replicability of preclinical findings. However, the technically feasible level of replicability remains unknown. To fill this gap, we conducted an analysis of data from the NCI60 cancer cell line screen (2.8 million compound/cell line experiments), which is to our knowledge the largest depository of experiments that have been repeatedly performed over decades. The findings revealed profound intra-laboratory data variability, although all experiments were executed following highly standardised protocols that avoid all known confounders of data quality. All compound/ cell line combinations with > 100 independent biological replicates displayed maximum GI50 (50% growth inhibition) fold changes (highest/ lowest GI50) > 5% and 70.5% displayed maximum fold changes > 1000. The highest maximum fold change was 3.16 × 10¹⁰ (lowest GI50: 7.93 ×10^-10 µM, highest GI50: 25.0 µM). FDA-approved drugs and experimental agents displayed similar variation. Variability remained high after outlier removal, when only considering experiments that tested drugs at the same concentration range, and when only considering NCI60-provided quality-controlled data. In conclusion, high variability is an intrinsic feature of anti-cancer drug testing, even among standardised experiments in a world-leading research environment. Awareness of this inherent variability will support realistic data interpretation and inspire research to improve data robustness. Further research will have to show whether the inclusion of a wider variety of model systems, such as animal and/ or patient-derived models, may improve data robustness.

Controlled amplification in oncology dose-finding trials

In oncology clinical trials the guiding principle of model-based dose-finding designs for cytotoxic agents is to progress as fast as possible towards, and identify, the dose level most likely to be the MTD. Recent developments with non-cytotoxic agents have broadened the scope of early phase trials to include multiple objectives. The ultimate goal of dose-finding designs in our modern era is to collect the relevant information in the study for final RP2D determination. While some information is collected on dose levels below and in the vicinity of the MTD during the escalation (using conventional tools such as the Continual Reassessment Method for example), designs that include expansion cohorts or backfill patients effectively amplify the information collected on the lower dose levels. This is achieved by allocating patients to dose levels slightly differently during the study in order to take into account the possibility that "less (dose) might be more". The objective of this paper is to study the concept of amplification. Under the heading of controlled amplification we can include dose expansion cohorts and backfill patients among others. We make some general observations by defining these concepts more precisely and study a specific design that exploits the concept of controlled amplification.

Improving early phase oncology clinical trial design: The case for finding the optimal biological dose

Historically early phase oncology drug development programmes have been based on the belief that "more is better". Furthermore, rule-based study designs such as the "3 + 3" design are still often used to identify the MTD. Phillips and Clark argue that newer Bayesian model-assisted designs such as the BOIN design should become the go to designs for statisticians for MTD finding. This short communication goes one stage further and argues that Bayesian model-assisted designs such as the BOIN12 which balances risk-benefit should be included as one of the go to designs for early phase oncology trials, depending on the study objectives. Identifying the optimal biological dose for future research for many modern targeted drugs, immunotherapies, cell therapies and vaccine therapies can save significant time and resources.

Patient-centered dosing: oncologists' perspectives about treatment-related side effects and individualized dosing for patients with metastatic breast cancer (MBC)

PURPOSE

Although metastatic breast cancer (MBC) is treatable, it is not curable and most patients remain on treatment indefinitely. While oncologists commonly prescribe the recommended starting dose (RSD) from the FDA-approved label, patient tolerance may differ from that seen in clinical trials. We report on a survey of medical oncologists' perspectives about treatment-related toxicity and willingness to discuss flexible dosing with patients.

METHODS

We disseminated a confidential survey via social media/email in Spring 2021. Eligible respondents needed to be US-based medical oncologists with experience treating patients with MBC.

RESULTS

Of 131 responses, 119 were eligible. Physicians estimated that 47% of their patients reported distressing treatment-related side effects; of these, 15% visited the Emergency Room/hospital and 37% missed treatment. 74% (n = 87) of doctors reported improvement of patient symptoms after dose reduction. 87% (n = 104) indicated that they had ever, if appropriate, initiated treatment at lower doses. Most (85%, n = 101) respondents did not believe that the RSD is always more effective than a lower dose and 97% (n = 115) were willing to discuss individualized dosing with patients.

CONCLUSION

Treatment-related side effects are prevalent among patients with MBC, resulting in missed treatments and acute care visits. To help patients tolerate treatment, oncologists may decrease initial and/or subsequent doses. The majority of oncologists reject the premise that a higher dose is always superior and are willing to discuss individualized dosing with patients. Given potential improvements regarding quality of life and clinical care, dose modifications should be part of routine shared decision-making between patients and oncologists.

Semimechanistic Physiologically-Based Pharmacokinetic/Pharmacodynamic Model Informing Epcoritamab Dose Selection for Patients With B-Cell Lymphomas

Epcoritamab is a CD3xCD20 bispecific antibody (bsAb) that induces T-cell-mediated cytotoxicity against CD20-positive B cells. Target engagement and crosslinking of CD3 and CD20 (trimer formation) leads to activation and expansion of T cells and killing of malignant B cells. The primary objective of the dose-escalation part of the phase I/II trial of epcoritamab was to determine the maximum tolerated dose, recommended phase II dose (RP2D), or both. For bsAbs, high target saturation can negatively affect trimer formation. The unique properties and mechanisms of action of bsAbs require novel pharmacokinetic (PK) modeling methods to predict clinical activity and inform RP2D selection. Traditional PK/pharmacodynamic (PD) modeling approaches are inappropriate because they may not adequately predict exposure-response relationships. We developed a semimechanistic, physiologically-based PK/PD model to quantitatively describe biodistribution, trimer formation, and tumor response using preclinical, clinical PK, biomarker, tumor, and response data from the dose-escalation part of the phase I/II trial. Clinical trial simulations were performed to predict trimer formation and tumor response in patients with diffuse large B-cell lymphoma (DLBCL) or follicular lymphoma (FL). Model-predicted trimer formation plateaued at doses of 48 to 96 mg. Simulation results suggest that the 48-mg dose may achieve optimal response rates in DLBCL and FL. Exposure-safety analyses showed a flat relationship between epcoritamab exposure and risk of cytokine release syndrome in the dose range evaluated. This novel PK/PD modeling approach guided selection of 48 mg as the RP2D and provides a framework that may be applied to other CD3 bsAbs.

Improving Dose-Optimization Processes Used in Oncology Drug Development to Minimize Toxicity and Maximize Benefit to Patients

This review highlights strategies to integrate dose optimization into premarketing drug development and discusses the underlying statistical principles. Poor dose optimization can have negative consequences for patients, most commonly because of toxicity, including poor quality of life, reduced effectiveness because of inability of patients to stay on current therapy or receive subsequent therapy because of toxicities, and difficulty in developing combination regimens. We reviewed US Food and Drug Administration initial approvals (2019-2021) of small molecules and antibody-drug conjugates for oncologic indications to determine the proportion with a recommended dosage at the maximum tolerated dose or the maximal administered dose, to characterize the use of randomized evaluations of multiple dosages in dose selection, to describe the frequency of dose modifications at the recommended dosage, and to identify case examples that highlight key principles for premarket dose optimization during drug development. Herein, we highlight major principles for dose optimization and review examples of recent US Food and Drug Administration approvals that illustrate how investigation of dose- and exposure-response relationships and use of randomized dose trials can support dose optimization. Although there has been some progress, dose optimization through randomized dose evaluation in oncology trials is not routinely conducted. Dose optimization is essential to ensure that patients receive therapies which maximize efficacy while minimizing toxicity.

Recent advances and clinical pharmacology aspects of Chimeric Antigen Receptor (CAR) T-cellular therapy development

Advances in immuno-oncology have provided a variety of novel therapeutics that harness the innate immune system to identify and destroy neoplastic cells. It is noteworthy that acceptable safety profiles accompany the development of these targeted therapies, which result in efficacious cancer treatment with higher survival rates and lower toxicities. Adoptive cellular therapy (ACT) has shown promising results in inducing sustainable remissions in patients suffering from refractory diseases. Two main types of ACT include engineered Chimeric Antigen Receptor (CAR) T cells and T cell receptor (TCR) T cells. The application of these immuno-therapies in the last few years has been successful and has demonstrated a safe and rapid treatment regimen for solid and non-solid tumors. The current review presents an insight into the clinical pharmacology aspects of immuno-therapies, especially CAR-T cells. Here, we summarize the current knowledge of TCR and CAR-T cell immunotherapy with particular focus on the structure of CAR-T cells, the effects and toxicities associated with these therapies in clinical trials, risk mitigation strategies, dose selection approaches, and cellular kinetics. Finally, the quantitative approaches and modeling techniques used in the development of CAR-T cell therapies are described.

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.

Leveraging modeling and simulation to optimize the therapeutic window for epigenetic modifier drugs

Dysregulated epigenetic processes can lead to altered gene expression and give rise to malignant transformation and tumorigenesis. Epigenetic drugs aim to revert the phenotype of cancer cells to normally functioning cells, and are developed and applied to treat both hematological and solid cancers. Despite this promising therapeutic avenue, the successful development of epigenetic modulators has been challenging. We argue that besides identifying the right responder patient population, the selection of an optimized dosing regimen is equally important. For the majority of epigenetic modulators, hematological adverse effects such as thrombocytopenia, anemia or neutropenia are frequently observed and may limit their therapeutic potential. Therefore, one of the key challenges is to identify a dosing regimen that maximizes drug efficacy and minimizes toxicity. This requires a good understanding of the quantitative relationship between the administered dose, the drug exposure and the magnitude and duration of drug response related to safety and efficacy. With case examples, we highlight how modeling and simulation has been successfully applied to address those questions. As an outlook, we suggest the combination of efficacy and safety prediction models that capture the quantitative, mechanistic relationships governing the balance between their safety and efficacy dynamics. A stepwise approach for its implementation is presented. Utilizing in silico explorations, the impact of dosing regimen on the therapeutic window can be explored. This will serve as a basis to select the most promising dosing regimen that maximizes efficacy while minimizing adverse effects and to increase the probability of success for the given epigenetic drug.

Optimal target saturation of ligand-blocking anti-GITR antibody IBI37G5 dictates FcγR-independent GITR agonism and antitumor activity

Glucocorticoid-induced tumor necrosis factor receptor (GITR) is a co-stimulatory receptor and an important target for cancer immunotherapy. We herein present a potent FcγR-independent GITR agonist IBI37G5 that can effectively activate effector T cells and synergize with anti-programmed death 1 (PD1) antibody to eradicate established tumors. IBI37G5 depends on both antibody bivalency and GITR homo-dimerization for efficient receptor cross-linking. Functional analyses reveal bell-shaped dose responses due to the unique 2:2 antibody-receptor stoichiometry required for GITR activation. Antibody self-competition is observed after concentration exceeded that of 100% receptor occupancy (RO), which leads to antibody monovalent binding and loss of activity. Retrospective pharmacokinetics/pharmacodynamics analysis demonstrates that the maximal efficacy is achieved at medium doses with drug exposure near saturating GITR occupancy during the dosing cycle. Finally, we propose an alternative dose-finding strategy that does not rely on the traditional maximal tolerated dose (MTD)-based paradigm but instead on utilizing the RO-function relations as biomarker to guide the clinical translation of GITR and similar co-stimulatory agonists.

Independent Drug Action in Combination Therapy: Implications for Precision Oncology

Combination therapies are superior to monotherapy for many cancers. This advantage was historically ascribed to the ability of combinations to address tumor heterogeneity, but synergistic interaction is now a common explanation as well as a design criterion for new combinations. We review evidence that independent drug action, described in 1961, explains the efficacy of many practice-changing combination therapies: it provides populations of patients with heterogeneous drug sensitivities multiple chances of benefit from at least one drug. Understanding response heterogeneity could reveal predictive or pharmacodynamic biomarkers for more precise use of existing drugs and realize the benefits of additivity or synergy.

BOIN: a novel Bayesian design platform to accelerate early phase brain tumor clinical trials

Despite decades of extensive research, the progress in developing effective treatments for primary brain tumors lags behind that of other cancers, largely due to the unique challenges of brain tumors (eg, the blood-brain barrier and high heterogeneity) that limit the delivery and efficacy of many therapeutic agents. One way to address this issue is to employ novel trial designs to better optimize the treatment regimen (eg, dose and schedule) in early phase trials to improve the success rate of subsequent phase III trials. The objective of this article is to introduce Bayesian optimal interval (BOIN) designs as a novel platform to design various types of early phase brain tumor trials, including single-agent and combination regimen trials, trials with late-onset toxicities, and trials aiming to find the optimal biological dose (OBD) based on both toxicity and efficacy. Unlike many novel Bayesian adaptive designs, which are difficult to understand and complicated to implement by clinical investigators, the BOIN designs are self-explanatory and user friendly, yet yield more robust and powerful operating characteristics than conventional designs. We illustrate the BOIN designs using a phase I clinical trial of brain tumor and provide software (freely available at www.trialdesign.org) to facilitate the application of the BOIN design.

Controlled backfill in oncology dose-finding trials

The use of backfill in early phase dose-finding trials is a relatively recent practice. It consists of assigning patients to dose levels below the level where the study is at. The main reason for backfilling is to collect additional pharmacokinetic, pharmacodynamic and response data, in order to assess whether a plateau may exist on the dose-efficacy curve. This is a possibility in oncology with molecularly targeted agents or immunotherapy. Recommending for further study a dose level lower than the maximum tolerated dose could be supported in such situations. How to best allocate backfill patients to dose levels is not yet established. In this paper we propose to randomise backfill patients below the dose level where the study is at. A refinement of this would be to stop backfilling to lower dose levels when these show insufficient efficacy compared to higher levels, starting at dose level 1 and repeating this process sequentially. At study completion, data from all patients (both backfill patients and dose-finding patients) is used to estimate the dose-response curve. The fit from a change point model is compared to the fit of a monotonic model to identify a potential plateau. Using simulations, we show that this approach can identify the plateau on the dose-response curve when such a plateau exists, allowing the recommendation of a dose level lower than the maximum tolerated dose for future studies. This contribution provides a methodological framework for backfilling, from the perspective of both design and analysis in early phase oncology trials.

Which factors matter the most? Revisiting and dissecting antibody therapeutic doses

Factors such as antibody clearance and target affinity can influence antibodies' effective doses for specific indications. However, these factors vary considerably across antibody classes, precluding direct and quantitative comparisons. Here, we apply a dimensionless metric, the therapeutic exposure affinity ratio (TEAR), which normalizes the therapeutic doses by antibody bioavailability, systemic clearance and target-binding property to enable direct and quantitative comparisons of therapeutic doses. Using TEAR, we revisited and dissected the doses of up to 60 approved antibodies. We failed to detect a significant influence of target baselines, turnovers or anatomical locations on antibody therapeutic doses, challenging the traditional perceptions. We highlight the importance of antibodies' modes of action for therapeutic doses and dose selections; antibodies that work through neutralizing soluble targets show higher TEARs than those working through other mechanisms. Overall, our analysis provides insights into the factors that influence antibody doses, and the factors that are crucial for antibodies' pharmacological effects.

The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia

The treatment of respiratory tract infections is threatened by the emergence of bacterial resistance. Immunomodulatory drugs, which enhance airway innate immune defenses, may improve therapeutic outcome. In this concept paper, we aim to highlight the utility of pharmacometrics and Bayesian inference in the development of immunomodulatory therapeutic agents as an adjunct to antibiotics in the context of pneumonia. For this, two case studies of translational modelling and simulation frameworks are introduced for these types of drugs up to clinical use. First, we evaluate the pharmacokinetic/pharmacodynamic relationship of an experimental combination of amoxicillin and a TLR4 agonist, monophosphoryl lipid A, by developing a pharmacometric model accounting for interaction and potential translation to humans. Capitalizing on this knowledge and associating clinical trial extrapolation and statistical modelling approaches, we then investigate the TLR5 agonist flagellin. The resulting workflow combines expert and prior knowledge on the compound with the in vitro and in vivo data generated during exploratory studies in order to construct high-dimensional models considering the pharmacokinetics and pharmacodynamics of the compound. This workflow can be used to refine preclinical experiments, estimate the best doses for human studies, and create an adaptive knowledge-based design for the next phases of clinical development.

Quantifying the limits of CAR T-cell delivery in mice and men

CAR (Chimeric Antigen Receptor) T cells have demonstrated clinical success for the treatment of multiple lymphomas and leukaemias, but not for various solid tumours, despite promising data from murine models. Lower effective CAR T-cell delivery rates to human solid tumours compared to haematological malignancies in humans and solid tumours in mice might partially explain these divergent outcomes. We used anatomical and physiological data for human and rodent circulatory systems to calculate the typical perfusion of healthy and tumour tissues, and estimated the upper limits of immune cell delivery rates across different organs, tumour types and species. Estimated maximum delivery rates were up to 10 000-fold greater in mice than humans yet reported CAR T-cell doses are typically only 10-100-fold lower in mice, suggesting that the effective delivery rates of CAR T cells into tumours in clinical trials are far lower than in corresponding mouse models. Estimated delivery rates were found to be consistent with published positron emission tomography data. Results suggest that higher effective human doses may be needed to drive efficacy comparable to mouse solid tumour models, and that lower doses should be tested in mice. We posit that quantitation of species and organ-specific delivery and homing of engineered T cells will be key to unlocking their potential for solid tumours.

Brigatinib Dose Rationale in Anaplastic Lymphoma Kinase-Positive Non-Small Cell Lung Cancer: Exposure-Response Analyses of Pivotal ALTA Study

Brigatinib is a kinase inhibitor indicated for patients with advanced anaplastic lymphoma kinase–positive non‐small cell lung cancer who progressed on or are intolerant to crizotinib. Approval was based on results from a randomized, dose‐ranging phase II study (ALK in Lung Cancer Trial of AP26113 (ALTA)). Despite an apparent dose–response relationship for efficacy in ALTA, an exposure–response relationship was not discernable using static models driven by time‐averaged exposure. However, exposure–response modeling using daily time‐varying area under the concentration curve as the predictor in time‐to‐event models predicted that increasing the dose of brigatinib (range, 30 mg once daily (q.d.) to 240 mg q.d.) would result in clinically meaningful improvements in progression‐free survival (PFS), intracranial PFS, and overall survival. Grade ≥ 2 rash and amylase elevation were predicted to significantly increase with brigatinib exposure. These results provided support for a favorable benefit‐risk profile with the approved dosing regimen (180 mg q.d. with 7‐day lead‐in at 90 mg) versus 90 mg q.d.

BOIN12: Bayesian Optimal Interval Phase I/II Trial Design for Utility-Based Dose Finding in Immunotherapy and Targeted Therapies

PURPOSE

For immunotherapy, such as checkpoint inhibitors and chimeric antigen receptor T-cell therapy, where the efficacy does not necessarily increase with the dose, the maximum tolerated dose may not be the optimal dose for treating patients. For these novel therapies, the objective of dose-finding trials is to identify the optimal biologic dose (OBD) that optimizes patients' risk-benefit trade-off.

METHODS

We propose a simple and flexible Bayesian optimal interval phase I/II (BOIN12) trial design to find the OBD that optimizes the risk-benefit trade-off. The BOIN12 design makes the decision of dose escalation and de-escalation by simultaneously taking account of efficacy and toxicity and adaptively allocates patients to the dose that optimizes the toxicity-efficacy trade-off. We performed simulation studies to evaluate the performance of the BOIN12 design.

RESULTS

Compared with existing phase I/II dose-finding designs, the BOIN12 design is simpler to implement, has higher accuracy to identify the OBD, and allocates more patients to the OBD. One of the most appealing features of the BOIN12 design is that its adaptation rule can be pretabulated and included in the protocol. During the trial conduct, clinicians can simply look up the decision table to allocate patients to a dose without complicated computation.

CONCLUSION

The BOIN12 design is simple to implement and yields desirable operating characteristics. It overcomes the computational and implementation complexity that plagues existing Bayesian phase I/II dose-finding designs and provides a useful design to optimize the dose of immunotherapy and targeted therapy. User-friendly software is freely available to facilitate the application of the BOIN12 design.

Immune Checkpoint Inhibitors in Melanoma: A Review of Pharmacokinetics and Exposure-Response Relationships

Immune checkpoint inhibitors are a new class of monoclonal antibodies that amplify T-cell-mediated immune responses against cancer cells. The introduction of these new drugs, first anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA4) and then anti-programmed death-1 (anti-PD1), was a major improvement in the treatment of advanced or metastatic melanoma, a highly immunogenic tumour. The development strategy for immune checkpoint immunotherapies differed from that traditionally used for cytotoxic therapies in oncology. The choices of doses at which to conduct clinical trials, and subsequently the choice of doses at which to use these new therapies, were not based on the identification of a maximum tolerated dose from dose-escalation studies; thus, pharmacokinetic and pharmacokinetic-pharmacodynamic modelling was essential. The studies conducted have shown that the pharmacokinetics of ipilimumab were linear and not time-dependent. In addition, there was a correlation between the trough concentrations of ipilimumab and its therapeutic efficacy. On the contrary, the anti-PD1 immunotherapies nivolumab and pembrolizumab had time-dependent pharmacokinetics. Their therapeutic efficacy was not related to their trough concentration, but there was a correlation between the clearance of anti-PD1 and the survival of melanoma patients. This review highlights the complexity of interpreting the exposure-response relationships of these agents. Further studies are needed to assess the value of therapeutic drug monitoring of immune checkpoint inhibitors in the treatment of melanoma.

Novel strategies in immune checkpoint inhibitor drug development: How far are we from the paradigm shift?

The development of immune checkpoint inhibitors (ICI) represents a major milestone in immune-oncology. Over the years these agents have demonstrated efficacy in an increasing array of malignancies. Despite this success however, significant challenges remain. Novel approaches to both drug development and trial design are required to incorporate the unique pharmacokinetic and pharmacodynamic properties of ICIs. Further, it has also been established that the benefit of ICIs is limited to only a subset of patients. The molecular interactions between native immune cells and tumorigenesis and progression represent an active area of biomarker research, and elucidating the mechanisms of response and resistance is crucial to develop rational trial designs for the next wave of immune-oncology (IO) clinical trials, particularly in patients with primary and/or acquired resistance. Efforts are now being made to integrate both biological and clinical information using novel multi-omic approaches which are now being developed to further elucidate the molecular signatures associated with IO treatment response and resistance and enable rational drug development and trial design processes. As such, precision IO and the ability to deliver patient-specific choices for ICI monotherapies or combination therapies has become an increasingly tangible goal. We herein describe the current landscape in ICI drug development and discuss the challenges and future directions in this exciting and evolving era in immune-oncology.

Slow-, Tight-Binding Inhibition of CYP17A1 by Abiraterone Redefines Its Kinetic Selectivity and Dosing Regimen

Substantial evidence underscores the clinical efficacy of inhibiting CYP17A1-mediated androgen biosynthesis by abiraterone for treatment of prostate oncology. Previous structural analysis and in vitro assays revealed inconsistencies surrounding the nature and potency of CYP17A1 inhibition by abiraterone. Here, we establish that abiraterone is a slow-, tight-binding inhibitor of CYP17A1, with initial weak binding preceding the subsequent slow isomerization to a high-affinity CYP17A1-abiraterone complex. The in vitro inhibition constant of the final high-affinity CYP17A1-abiraterone complex ( ( K _i * = 0.39 nM )yielded a binding free energy of -12.8 kcal/mol that was quantitatively consistent with the in silico prediction of -14.5 kcal/mol. Prolonged suppression of dehydroepiandrosterone (DHEA) concentrations observed in VCaP cells after abiraterone washout corroborated its protracted CYP17A1 engagement. Molecular dynamics simulations illuminated potential structural determinants underlying the rapid reversible binding characterizing the two-step induced-fit model. Given the extended residence time (42 hours) of abiraterone within the CYP17A1 active site, in silico simulations demonstrated sustained target engagement even when most abiraterone has been eliminated systemically. Subsequent pharmacokinetic-pharmacodynamic (PK-PD) modeling linking time-dependent CYP17A1 occupancy to in vitro steroidogenic dynamics predicted comparable suppression of downstream DHEA-sulfate at both 1000- and 500-mg doses of abiraterone acetate. This enabled mechanistic rationalization of a clinically reported PK-PD disconnect, in which equipotent reduction of downstream plasma DHEA-sulfate levels was achieved despite a lower systemic exposure of abiraterone. Our novel findings provide the impetus for re-evaluating the current dosing paradigm of abiraterone with the aim of preserving PD efficacy while mitigating its dose-dependent adverse effects and financial burden. SIGNIFICANCE STATEMENT: With the advent of novel molecularly targeted anticancer modalities, it is becoming increasingly evident that optimal dose selection must necessarily be predicated on mechanistic characterization of the relationships between target exposure, drug-target interactions, and pharmacodynamic endpoints. Nevertheless, efficacy has always been perceived as being exclusively synonymous with affinity-based measurements of drug-target binding. This work demonstrates how elucidating the slow-, tight-binding inhibition of CYP17A1 by abiraterone via in vitro and in silico analyses was pivotal in establishing the role of kinetic selectivity in mediating time-dependent CYP17A1 engagement and eventually downstream efficacy outcomes.

Assessing the translational value of pre-clinical studies for clinical response rate in oncology: an exploratory investigation of 42 FDA-approved small-molecule targeted anticancer drugs

PURPOSE

To assess the translational value of anticancer preclinical models, we retrospectively investigated the relationships between preclinical data and clinical response rate for 42 small-molecule targeted anticancer drugs approved by the US FDA from 2001 to 2018.

METHODS

For 42 FDA-approved drugs, relevant pre-clinical (IC₅₀, mouse PK/efficacy) and clinical (overall response rates [ORR], PK) data were extracted from the public domain. Relationships were investigated overall and separately by mechanism of action and solid vs liquid tumors. Binomial-normal regression analysis was performed using R.

RESULTS

A significant correlation was found between the ratio of free human average plasma concentration (hC_ave) at the approved clinical dose to biochemical IC₅₀ and ORR for kinase inhibitors with solid tumor indications (KI_ST). We also identified that, for KI_ST, the ratios of (i) total and (ii) free human-to-mouse average plasma concentration at efficacious doses were correlated to ORR ((i) R² = 0.72, n = 10; (ii) R² = 0.78, n = 10)).

CONCLUSION

Relationships were identified for ratios of efficacious clinical exposures to typical preclinical pharmacology data and ORR for KI_ST in this retrospective analysis. Although the obtained datasets are limited, the relationships demonstrate that a systemic exposure relative to established pre-clinical pharmacology experiments for an investigational KI_ST could be used as a reference to assess if desired efficacy could be achieved. This approach may assist selection of the recommended phase 2 dose (RP2D) of an investigational drug.

Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities

Optimal control theory is branch of mathematics that aims to optimize a solution to a dynamical system. While the concept of using optimal control theory to improve treatment regimens in oncology is not novel, many of the early applications of this mathematical technique were not designed to work with routinely available data or produce results that can eventually be translated to the clinical setting. The purpose of this review is to discuss clinically relevant considerations for formulating and solving optimal control problems for treating cancer patients. Our review focuses on two of the most widely used cancer treatments, radiation therapy and systemic therapy, as they naturally lend themselves to optimal control theory as a means to personalize therapeutic plans in a rigorous fashion. To provide context for optimal control theory to address either of these two modalities, we first discuss the major limitations and difficulties oncologists face when considering alternate regimens for their patients. We then provide a brief introduction to optimal control theory before formulating the optimal control problem in the context of radiation and systemic therapy. We also summarize examples from the literature that illustrate these concepts. Finally, we present both challenges and opportunities for dramatically improving patient outcomes via the integration of clinically relevant, patient-specific, mathematical models and optimal control theory.