A Bayesian pharmacokinetics integrated phase I-II design to optimize dose-schedule regimes.

The schedule of administering a drug has profound impact on the toxicity and efficacy profiles of the drug through changing its pharmacokinetics (PK). PK is an innate and indispensable component of the dose-schedule optimization. Motivated by this, we propose a Bayesian PK integrated dose-schedule finding (PKIDS) design to identify the optimal dose-schedule regime by integrating PK, toxicity, and efficacy data. Based on the causal pathway that dose and schedule affect PK, which in turn affects efficacy and toxicity, we jointly model the three endpoints by first specifying a Bayesian hierarchical model for the marginal distribution of the longitudinal dose-concentration process. Conditional on the drug concentration in plasma, we jointly model toxicity and efficacy as a function of the concentration. We quantify the risk-benefit of regimes using utility-continuously updating the estimates of PK, toxicity, and efficacy based on interim data-and make adaptive decisions to assign new patients to appropriate dose-schedule regimes via adaptive randomization. The simulation study shows that the PKIDS design has desirable operating characteristics.

Pharmacokinetics and pharmacodynamics of intravenous delafloxacin in healthy subjects: model-based dose optimization.

Delafloxacin, a fluoroquinolone antibiotic to treat skin infections, exhibits a broad-spectrum antimicrobial activity. The first randomized, open-label phase I clinical trial was conducted to assess the safety and pharmacokinetics (PK) of intravenous delafloxacin in the Chinese population. A population pharmacokinetic (PopPK) model based on the clinical trial was conducted by NONMEM software. Monte Carlo simulation was performed to evaluate the antibacterial effects of delafloxacin at different doses in different Chinese populations. The PK characteristics of delafloxacin were best described by a three-compartment model with mixed linear and nonlinear clearance. Body weight was included as a covariate in the model. We simulated the AUC0-24h in a steady state at five doses in patient groups of various weights. The results indicated that for patients weighing 70 kg and treated with methicillin-resistant Staphylococcus aureus (MRSA) infections, a minimum dose of 300 mg achieved a PTA > 90% at MIC90 of 0.25 µg/mL, suggesting an ideal bactericidal effect. For patients weighing less than 60 kg, a dose of 200 mg achieved a PTA > 90% at MIC90 of 0.25 µg/mL, also suggesting an ideal bactericidal effect. Additionally, this trial demonstrated the high safety of delafloxacin in single-dose and multiple-dose groups of Chinese. Delafloxacin (300 mg, q12h, iv) was recommended for achieving optimal efficacy in Chinese bacterial skin infections patients. To ensure optimal efficacy, an individualized dose of 200 mg (q12h, iv) could be advised for patients weighing less than 60 kg, and 300 mg (q12h, iv) for those weighing more than 60 kg.

Comparative pharmacodynamics and dose optimization of liposomal amphotericin B against Candida species in an in vitro pharmacokinetic/pharmacodynamic model.

As comparative pharmacokinetic/pharmacodynamic (PK/PD) studies of liposomal amphotericin B (L-AMB) against Candida spp. are lacking, we explored L-AMB pharmacodynamics against different Candida species in an in vitro PK/PD dilution model. Eight Candida glabrata, Candida parapsilosis, and Candida krusei isolates (EUCAST/CLSI AMB MIC 0.125-1 mg/L) were studied in the in vitro PK/PD model simulating L-AMB Cmax = 0.25-64 mg/L and t1/2 = 9 h. The model was validated with one susceptible and one resistant Candida albicans isolate. The Cmax/MIC-log10CFU/mL reduction from the initial inoculum was analyzed with the Emax model, and Monte Carlo analysis was performed for the standard (3 mg/kg with Cmax = 21.87 ± 12.47 mg/L) and higher (5 mg/kg with Cmax = 83 ± 35.2 mg/L) L-AMB dose. A ≥1.5 log10CFU/mL reduction was found at L-AMB Cmax = 8 mg/L against C. albicans, C. parapsilosis, and C. krusei isolates (MIC 0.25-0.5 mg/L) whereas L-AMB Cmax ≥ 32 mg/L was required for C. glabrata isolates. The in vitro PK/PD relationship followed a sigmoidal pattern (R2 ≥ 0.85) with a mean Cmax/MIC required for stasis of 2.1 for C. albicans (close to the in vivo stasis), 24/17 (EUCAST/CLSI) for C. glabrata, 8 for C. parapsilosis, and 10 for C. krusei. The probability of target attainment was ≥99% for C. albicans wild-type (WT) isolates with 3 mg/kg and for wild-type isolates of the other species with 5 mg/kg. L-AMB was four- to eightfold less active against the included non-C. albicans species than C. albicans. A standard 3-mg/kg dose is pharmacodynamically sufficient for C. albicans whereas our data suggest that 5 mg/kg may be recommendable for the included non-C. albicans species.

How to improve treatment-free remission eligibility in chronic myeloid leukaemia?

The achievement of treatment-free remission (TFR) has become a significant clinical end-point in the management of patients with chronic myeloid leukaemia (CML), providing an opportunity to discontinue therapy with tyrosine kinase inhibitors (TKIs) while maintaining deep molecular response (DMR). Early studies, such as the French STIM trial, have demonstrated that a portion of patients can maintain DMR after treatment cessation, with rates ranging from 40% to 50%, and most relapses occurring within the first 6 months. Key prognostic factors for successful TFR, including treatment duration, duration of DMR, risk scores, and transcript type, have been identified. Optimal patient selection for TFR remains a challenge, but recent research provides insights into potential strategies to increase TFR eligibility. Evidence suggests that early intervention switching to achieve optimal response, treatment combinations, proactive switch in the case of absence of DMR, dose-optimization and induction-maintenance approach can improve molecular responses and, consequently, enhance TFR eligibility. In this review, we report and discuss all the potential therapeutic strategies that may enhance eligibility for a first attempt at TFR, with a particular emphasis on potential future approaches.

Choice of Frontline Tyrosine-Kinase Inhibitor and Early Events in Very Elderly Patients With Chronic Myeloid Leukemia in Chronic Phase: A "Campus CML" Study.

OBJECTIVES: The study aimed to evaluate the utilization of frontline TKI therapy in a large cohort of elderly CP-CML patients. METHODS: A retrospective analysis was conducted on 332 CP-CML patients aged 75 years or older among 1929 diagnosed from January 2012 to December 2019 followed at 36 participating Hematology Centers involved in the "Campus CML" project. RESULTS: Among the patients analyzed, 85.8% received imatinib (IM) while 14.2% received second-generation TKIs (2G-TKI), 59.5% dasatinib, and 40.5% nilotinib. Most patients initiated IM at standard dose (67.3%) while 32.7% at reduced dose. A similar trend was observed with 2G-TKIs. The cumulative incidence of permanent TKI discontinuation at 12 months was 28.4%, primarily due to primary resistance (10.1%) and extra-hematologic toxicity (9.5%), with no significant difference between IM and 2G-TKI groups. Following the introduction of generic IM in Italy in 2018, IM usage increased significantly compared with 2G-TKIs. CONCLUSIONS: IM was in our Centers the preferred frontline therapy for older CP-CML patients, with increasing utilization after the introduction of generic formulations. However, 2G-TKIs are still used in a substantial proportion of patients, suggesting individualized physician assessments regarding patient suitability and expectations. Further investigation is needed to assess efficacy and safety of reduced TKI doses in this patient population.

A seamless phase II/III design with dose optimization for oncology drug development.

The US FDA's Project Optimus initiative that emphasizes dose optimization prior to marketing approval represents a pivotal shift in oncology drug development. It has a ripple effect for rethinking what changes may be made to conventional pivotal trial designs to incorporate a dose optimization component. Aligned with this initiative, we propose a novel seamless phase II/III design with dose optimization (SDDO framework). The proposed design starts with dose optimization in a randomized setting, leading to an interim analysis focused on optimal dose selection, trial continuation decisions, and sample size re-estimation (SSR). Based on the decision at interim analysis, patient enrollment continues for both the selected dose arm and control arm, and the significance of treatment effects will be determined at final analysis. The SDDO framework offers increased flexibility and cost-efficiency through sample size adjustment, while stringently controlling the Type I error. This proposed design also facilitates both accelerated approval (AA) and regular approval in a "one-trial" approach. Extensive simulation studies confirm that our design reliably identifies the optimal dosage and makes preferable decisions with a reduced sample size while retaining statistical power.

REDOMA: Bayesian random-effects dose-optimization meta-analysis using spike-and-slab priors.

The rise of cutting-edge precision cancer treatments has led to a growing significance of the optimal biological dose (OBD) in modern oncology trials. These trials now prioritize the consideration of both toxicity and efficacy simultaneously when determining the most desirable dosage for treatment. Traditional approaches in early-phase oncology trials have conventionally relied on the assumption of a monotone relationship between treatment efficacy and dosage. However, this assumption may not hold valid for novel oncology therapies. In reality, the dose-efficacy curve of such treatments may reach a plateau at a specific dose, posing challenges for conventional methods in accurately identifying the OBD. Furthermore, achieving reliable identification of the OBD is typically not possible based on a single small-sample trial. With data from multiple phase I and phase I/II trials, we propose a novel Bayesian random-effects dose-optimization meta-analysis (REDOMA) approach to identify the OBD by synthesizing toxicity and efficacy data from each trial. The REDOMA method can address trials with heterogeneous characteristics. We adopt a curve-free approach based on a Gamma process prior to model the average dose-toxicity relationship. In addition, we utilize a Bayesian model selection framework that uses the spike-and-slab prior as an automatic variable selection technique to eliminate monotonic constraints on the dose-efficacy curve. The good performance of the REDOMA method is confirmed by extensive simulation studies.

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.

Administration mode matters for 5-fluorouracil therapy: Physiologically based pharmacokinetic evidence for avoidance of myelotoxicity by continuous infusion but not intravenous bolus.

AIMS: Pre-emptive prediction to avoid myelosuppression and harmful sequelae is difficult given the complex interplay among patients, drugs and treatment protocols. This study aimed to model plasma and bone marrow concentrations and the likelihood of myelotoxicity following administration of 5-fluorouracil (5-FU) by diverse intravenous (IV) bolus or continuous infusion (cIF) regimens. METHODS: Using physicochemical, in vitro and clinical data obtained from the literature consisting of various regimens and patient cohorts, a 5-FU physiologically based pharmacokinetic (PBPK) model was developed. The predicted and observed PK values were compared to assess model performance prior to examining myelotoxicity potential of IV bolus vs. cIF and DPYD wild type vs. genetic variant. RESULTS: The established model was verified by utilizing 5-FU concentration-time profiles of adequate heterogeneity contributed by 36 regimens from 15 studies. The study provided corroborative evidence to explain why cIF (vs. IV bolus) had lower myelotoxicity risk despite much higher total doses. The PBPK model was used to estimate the optimal dosage in patients heterozygous for the DPYD c.1905 + 1G > A allele and suggested that a dose reduction of at least 25% was needed (compared to the dose in wild-type subjects). CONCLUSION: A verified PBPK model was used to explain the lower myelotoxicity risk of cIF vs. IV bolus administration of 5-FU and to estimate the dose reduction needed in carriers of a DPYD variant. With appropriate data, expertise and resources, PBPK models have many potential uses in precision medicine application of oncology drugs.

How to handle a missed or delayed dose of lacosamide in pediatric patients with epilepsy? a mode-informed individual dosing.

This study aims to investigate the effects on the pharmacokinetic (PK) of lacosamide (LCM), and to guide the individual dosing regimens for children and ones with poor medication adherence. Population PK research was performed based on 164 plasma samples of 113 pediatric patients aged from 1.75 to 14.42 years old. The PK characteristic of LCM was developed by a one-compartment model with first-order elimination. The typical value of apparent clearance (CL) and apparent volume of distribution (Vd) was 1.91 L·h-1 and 56.53 L respectively. In the final model, the variability of CL was significantly associated with the body surface area (BSA) and elevated uric acid (UA) level. In contrast, the impact of some prevalent anti-seizure medicines, such as valproic acid, levetiracetam, oxcarbazepine, lamotrigine, and perampanel, and gene polymorphisms of Cytochrome P450 (CYP)2C19, ATP-binding cassette (ABC)B1, and ABCC2 had no clinical significance on the PK parameters of LCM. BSA-based dosing regimen of LCM was provided according to Monte Carlo simulation approach; while the dosage should reduce half in patients with an UA level of more than 400 µmol·L-1 comparing with an UA level of 100 µmol·L-1. Individualize remedial doses of about 0.5- to 1.5-fold of regular doses were recommended in six common scenarios of missed or delayed doses, that depended on the delayed time. In current study, the population PK model of LCM in children with epilepsy was developed successfully. The BSA-based dosing regimen and individualized remedial strategy were recommended to guarantee the precise administration of LCM.

Population pharmacokinetics and dosage optimization of linezolid in Chinese older patients.

PURPOSE: To assess the pharmacokinetics and pharmacodynamics of linezolid in a retrospective cohort of hospitalized Chinese older patients. METHODS: Patients > 60 years of age, who received intravenous linezolid (600 mg), were included. A population pharmacokinetics (PPK) model was established using nonlinear mixed-effects modeling. The predictive performance of the final model was assessed using goodness-of-fit plots, bootstrap analyses, and visual predictive checks. Monte Carlo simulations were used to evaluate the achievement of a pharmacodynamics target for the area under the serum concentration-time curve/minimum inhibitory concentration (AUC0-24/MIC). RESULTS: A total of 210 samples were collected from 120 patients. A one-compartment PPK model with linear elimination best predicted the linezolid plasma concentrations. Linezolid clearance (CL) was 4.22 L h-1 and volume of distribution (Vd) was 45.80 L; serum uric acid (SUA) was a significant covariate of CL. CONCLUSION: The results of this study indicated that the standard dose was associated with a risk of overexposure in older patients, particularly those with high SUA values; these patients would benefit from a lower dose (300 mg every 12 h).

Multi-institutional study of osimertinib dose-optimization in non-small cell lung cancer patients with EGFR activating mutation aged 70 years or older ('MONEY' trial).

Osimertinib is the standard of care for patients with epidermal growth factor receptor-activating mutation-positive non-small cell lung cancer. Dose-toxicity has been previously reported, but no dose-response data within the range of 20-240 mg daily (mg/d). Thus, the current 80 mg/d dosing might be too high for elderly Japanese patients with an average body weight of only 50 kg, resulting in excessive toxicity and cost. We therefore initiated a study to investigate whether osimertinib at 40 mg/d is non-inferior to 80 mg/d in patients with advanced or recurrent epidermal growth factor receptor-activating mutation-positive non-small cell lung cancer aged ≥70 years, using a regression discontinuity design. Osimertinib is administered at 40 mg/d for body weight ≤50 kg, and 80 mg/d for body weight >50 kg. The primary endpoint is progression-free survival. Sample size is 550 patients, based on a non-inferiority margin of the progression-free survival hazard ratio 1.333, 0.10 one-sided type I error and 80% power.

Current issues in dose-finding designs: A response to the US Food and Drug Adminstration's Oncology Center of Excellence Project Optimus.

With the advent of targeted agents and immunological therapies, the medical research community has become increasingly aware that conventional methods for determining the best dose or schedule of a new agent are inadequate. It has been well established that conventional phase I designs cannot reliably identify safe and effective doses. This problem applies, generally, for cytotoxic agents, radiation therapy, targeted agents, and immunotherapies. To address this, the US Food and Drug Administration's Oncology Center of Excellence initiated Project Optimus, with the goal "to reform the dose optimization and dose selection paradigm in oncology drug development." As a response to Project Optimus, the articles in this special issue of Clinical Trials review recent advances in methods for choosing the dose or schedule of a new agent with an overall objective of informing clinical trialists of these innovative designs. This introductory article briefly reviews problems with conventional methods, the regulatory changes that encourage better dose optimization designs, and provides brief summaries of the articles that follow in this special issue.

Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies.

Targeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of "more is better" is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I-II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I-II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I-II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose-outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I-II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.

Dose optimization for cancer treatments with considerations for late-onset toxicities.

Given that novel anticancer therapies have different toxicity profiles and mechanisms of action, it is important to reconsider the current approaches for dose selection. In an effort to move away from considering the maximum tolerated dose as the optimal dose, the Food and Drug Administration Project Optimus points to the need of incorporating long-term toxicity evaluation, given that many of these novel agents lead to late-onset or cumulative toxicities and there are no guidelines on how to handle them. Numerous methods have been proposed to handle late-onset toxicities in dose-finding clinical trials. A summary and comparison of these methods are provided. Moreover, using PI3K inhibitors as a case study, we show how late-onset toxicity can be integrated into the dose-optimization strategy using current available approaches. We illustrate a re-design of this trial to compare the approach to those that only consider early toxicity outcomes and disregard late-onset toxicities. We also provide proposals going forward for dose optimization in early development of novel anticancer agents with considerations for late-onset toxicities.

Adaptive Bayesian information borrowing methods for finding and optimizing subgroup-specific doses.

In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.

The patient perspective on dose optimization for anticancer treatments: A new era of cancer drug dosing-Challenging the "more is better" dogma.

The Patient-Centered Dosing Initiative, a patient-led effort advocating for a paradigm shift in determining cancer drug dosing strategies, pioneers a departure from traditional oncology drug dosing practices. Historically, oncology drug dosing relies on identifying the maximum tolerated dose through phase 1 dose escalation methodology, favoring higher dosing for greater efficacy, often leading to higher toxicity. However, this approach is not universally applicable, especially for newer treatments like targeted therapies and immunotherapies. Patient-Centered Dosing Initiative challenges this "more is better" ethos, particularly as metastatic breast cancer patients themselves, as they not only seek longevity but also a high quality of life since most metastatic breast cancer patients stay on treatment for the rest of their lives. Surveying 1221 metastatic breast cancer patients and 119 oncologists revealed an evident need for flexible dosing strategies, advocating personalized care discussions based on patient attributes. The survey results also demonstrated an openness toward flexible dosing and a willingness from both patients and clinicians to discuss dosing as part of their care. Patient-centered dosing emphasizes dialogue between clinicians and patients, delving into treatment efficacy-toxicity trade-offs. Similarly, clinical trial advocacy for multiple dosing regimens encourages adaptive strategies, moving away from strict adherence to maximum tolerated dose, supported by recent research in optimizing drug dosages. Recognizing the efficacy-effectiveness gap between clinical trials and real-world practice, Patient-Centered Dosing Initiative underscores the necessity for patient-centered dosing strategies. A focus on individual patient attributes aligns with initiatives like Project Optimus and Project Renewal, aiming to optimize drug dosages for improved treatment outcomes at both the pre- and post-approval phases. Patient-Centered Dosing Initiative's efforts extend to patient education, providing tools to initiate dosage-related conversations with physicians. In addition, it emphasizes physician-patient dialogues and post-marketing studies as essential in determining optimal dosing and refining drug regimens. A dose-finding paradigm prioritizing drug safety, tolerability, and efficacy benefits all stakeholders, reducing emergency care needs and missed treatments for patients, aligning with oncologists' and patients' shared goals. Importantly, it represents a win-win scenario across healthcare sectors. In summary, the Patient-Centered Dosing Initiative drives transformative changes in cancer drug dosing, emphasizing patient well-being and personalized care, aiming to enhance treatment outcomes and optimize oncology drug delivery.

Population pharmacokinetics and dose optimization of magnesium sulfate in Chinese preeclampsia population.

OBJECTIVE: To establish the population pharmacokinetics (PPK) of magnesium sulfate (MgSO4)in women with preeclampsia (PE), and to determine the key covariates having an effect in magnesium pharmacokinetics in Chinese PE. METHODS: Pregnant women with PE prescribed MgSO4 were enrolled in this prospective study from April 2021 to April 2023. On the initial day of administration, the patients were administered a loading dose of 5 g in conjunction with 10 g of magnesium sulfate as a maintenance dose. On the second day, only the maintenance dose was administration, and maternal blood samples were taken at 0, 4, 5, and 12 h after the second day's 10 g maintenance dose. The software Phoenix was used to estimate PPK parameters of MgSO4, such as clearance (CL) and volume of distribution (V), and to model PPK models with patient demographic, clinical, and laboratory covariates. RESULTS: A total of 199 blood samples were collected from 51 women with PE and PPK profiles were analyzed. The PPK of MgSO4 is consistent with to a one-compartment model. The base model adequately described the maternal serum magnesium concentrations after magnesium administration. The population parameter estimates were as follows: CL was 2.98 L/h, V was 25.07 L. The model predictions changed significantly with covariates (BMI, creatinine clearance, and furosemide). Furosemide statistically influences V. The creatinine clearance, BMI and furosemide jointly affects CL. Monte Carlo simulation results showed that a loading dose combined with a maintenance dose would need to be administered daily to achieve the therapeutic blood magnesium concentrations. For the non-furosemide group, the optimal dosing regimen was a 5 g loading dose combined with a 10 g maintenance dose of MgSO4. For the furosemide group, the optimal dosing regimen was a 2.5 g loading dose combined with a 10 g maintenance dose of MgSO4. CONCLUSIONS: The magnesium PPK model was successfully developed and evaluated in Chinese preeclampsia population, and the dose optimization of MgSO4 was completed through Monte Carlo simulation.

Strategies for successful dose optimization in oncology drug development: a practical guide.

Dose optimization is a critical challenge in drug development. Historically, dose determination in oncology has followed a divergent path from other non-oncology therapeutic areas due to the unique characteristics and requirements in Oncology. However, with the emergence of new drug modalities and mechanisms of drugs in oncology, such as immune therapies, radiopharmaceuticals, targeted therapies, cytostatic agents, and others, the dose-response relationship for efficacy and toxicity could be vastly varied compared to the cytotoxic chemotherapies. The doses below the MTD may demonstrate similar efficacy to the MTD with an improved tolerability profile, resembling what is commonly observed in non-oncology treatments. Hence, alternate strategies for dose optimization are required for new modalities in oncology drug development. This paper delves into the historical evolution of dose finding methods from non-oncology to oncology, highlighting examples and summarizing the underlying drivers of change. Subsequently, a practical framework and guidance are provided to illustrate how dose optimization can be incorporated into various stages of the development program. We provide the following general recommendations: 1) The objective for phase I is to identify a dose range rather than a single MTD dose for subsequent development to better characterize the safety and tolerability profile within the dose range. 2) At least two doses separable by PK are recommended for dose optimization in phase II. 3) Ideally, dose optimization should be performed before launching the confirmatory study. Nevertheless, innovative designs such as seamless II/III design can be implemented for dose selection and may accelerate the drug development program.

Leveraging pharmacokinetic parameters as covariate in Bayesian logistic regression model to optimize dose selection in early phase oncology trial.

Dose selection and optimization in early phase of oncology drug development serves as the foundation for the success of late phases drug development. Bivariate Bayesian logistic regression model (BLRM) is a widely utilized model-based algorithm that has been shown to improve the accuracy for identifying recommended phase 2 dose (RP2D) based on dose-limiting-toxicity (DLT) over traditional method such as 3 + 3. However, it remains a challenge to optimize dose selection that strikes a proper balance between safety and efficacy in escalation and expansion phase of phase I trials. In this paper, we first use a phase I clinical trial to demonstrate how the variability of drug exposure related to pharmacokinetic (PK) parameters among trial participants may add to the difficulties of identifying optimal dose. We use simulation to show that concurrently or retrospectively fitting BLRM model for dose/toxicity data from escalation phase with dose-independent PK parameters as covariate lead to improved accuracy of identifying dose level at which DLT rate is within a prespecified toxicity interval. Furthermore, we proposed both model- and rule-based methods to modify dose at patient level in expansion cohorts based on their PK/exposure parameters. Simulation studies show this approach leads to higher likelihood for a dose level with a manageable toxicity and desirable efficacy margin to be advanced to late phase pipeline after being screened at expansion phase of phase I trial.