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We propose a Bayesian optimal phase 2 design for jointly monitoring efficacy and toxicity, referred to as BOP2-TE, to improve the operating characteristics of the BOP2 design proposed by Zhou. BOP2-TE utilizes a Dirichlet-multinomial model to jointly model the distribution of toxicity and efficacy endpoints, making go/no-go decisions based on the posterior probability of toxicity and futility. In comparison to the original BOP2 and other existing designs, BOP2-TE offers the advantage of providing rigorous type I error control in cases where the treatment is toxic and futile, effective but toxic, or safe but futile, while optimizing power when the treatment is effective and safe. As a result, BOP2-TE enhances trial safety and efficacy. We also explore the incorporation of BOP2-TE into multiple-dose randomized trials for dose optimization, and consider a seamless design that integrates phase I dose finding with phase II randomized dose optimization. BOP2-TE is user-friendly, as its decision boundary can be determined prior to the trial’s onset. Simulations demonstrate that BOP2-TE possesses desirable operating characteristics. We have developed a user-friendly web application as part of the BOP2 app, which is freely available at https://www.trialdesign.org. [ABSTRACT FROM AUTHOR]

ABSTRACT The primary purpose of an oncology dose‐finding trial for novel anticancer agents has been shifting from determining the maximum tolerated dose to identifying an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. In 2022, the FDA Oncology Center of Excellence initiated Project Optimus to reform the paradigm of dose optimization and dose selection in oncology drug development and issued a draft guidance. The guidance suggests that dose‐finding trials include randomized dose–response cohorts of multiple doses and incorporate information on pharmacokinetics (PK) in addition to safety and efficacy data to select the OD. Furthermore, PK information could be a quick alternative to efficacy data to predict the minimum efficacious dose and decide the dose assignment. This article proposes a model‐based trial design for dose optimization with a randomization scheme based on PK outcomes in oncology. A simulation study shows that the proposed design has advantages compared to the other designs in the percentage of correct OD selection and the average number of patients assigned to OD in various realistic settings. [ABSTRACT FROM AUTHOR]

Background: Incorporating backfill cohorts in phase I oncology trials is a recently developed strategy for dose optimization. However, the efficacy assessment window is long in general, causing a lag in identifying ineffective doses and more patients being backfilled to those doses. There is necessity to investigate how to use patient-reported outcomes (PRO) to determine doses for backfill cohorts. Methods: We propose a unified Bayesian design framework, called 'Backfill-QoL', to utilize patient-reported quality of life (QoL) data into phase I oncology trials with backfill cohorts, including methods for trial monitoring, algorithm for dose-finding, and criteria for dose selection. Simulation studies and sensitivity analyses are conducted to evaluate the proposed Backfill-QoL design. Results: The simulation studies demonstrate that the Backfill-QoL design is more efficient than traditional dose-expansion strategy, and fewer patients would be allocated to doses with unacceptable QoL profiles. A user-friendly Windows desktop application is developed and freely available for implementing the proposed design. Conclusions: The Backfill-QoL design enables continuous monitoring of safety, efficacy and QoL outcomes, and the recommended phase II dose (RP2D) can be identified in a more patient-centered perspective. [ABSTRACT FROM AUTHOR]

In response to the growing demand for environmentally sustainable agricultural practices that enhance production efficiency, this study aimed to determine the optimal application rates of bovine rumen content (CBR) and chicken litter (CCL) as organic composts. Conducted in a controlled greenhouse environment, the research assessed the effects of various doses of these composts on soil chemical and microbiological properties and on the growth and productivity of yellow biquinho pepper (Capsicum chinense Jacq.). The results of this study confirm that organic composts improve soil health and increase the growth and productivity of yellow biquinho pepper. However, while CBR consistently enhances plant growth across a range of doses, CCL shows a dose-dependent response, with the highest fruit production achieved at an application rate of 73.92 Mg ha−1. Although this dose of CCL is beneficial for increasing yield, it also marks a threshold beyond which further increases may lead to negative effects such as nutrient saturation or salt accumulation, potentially resulting in soil toxicity. The study supports the use of CBR and CCL as effective soil conditioners and emphasizes the need to identify the optimal compost dose to efficiently harness the benefits of organic composts in sustainable agricultural practices. [ABSTRACT FROM AUTHOR]

Copyright of Die Radiologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ABSTRACT Objectives Methods Results Conclusions The study aimed to evaluate the utilization of frontline TKI therapy in a large cohort of elderly CP‐CML patients.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.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.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. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

It remains unclear whether dosage adjustment of intravenous lidocaine is necessary during general anesthesia for elderly patients over 75 years old. This study aimed to investigate the effects of age on the pharmacokinetics (PK) and safety of intravenous lidocaine in patients undergoing general anesthesia. A total of 599 plasma samples were collected from 76 general anesthesia patients across three age groups: 18–64, 65–74, and ≥ 75 years. Lidocaine was administered intravenously at a dose of 1.5 mg/kg for the 18–64 and 65–74 years groups, while the dose was adjusted to 1.0 mg/kg for the ≥ 75 years group. The plasma concentrations of lidocaine and its active metabolites were measured using a validated ultra-performance liquid chromatography-tandem mass spectrometry assay, and the data were analyzed using a noncompartmental analysis. The results revealed no significant age-related differences in the PK of lidocaine and its metabolites. Among the three age groups, over 90 % of patient achieved a lidocaine concentration within a safe and effective range when the dosage was normalized to 1.5 mg/kg. In conclusion, age-based dosage adjustment was unnecessary for intravenous lidocaine in patients below 86 years undergoing general anesthesia. [ABSTRACT FROM AUTHOR]

Introduction: Although oncology has seen large scientific and clinical advances over the last decade, it also has one of the lowest success rates for novel agents across therapeutic areas. Adaptive clinical trial design has been a popular option for increasing clinical trial efficiency and the chances of trial success. Seamless clinical trial design are studies in which two or more clinical trial phases are combined into a single study with a pre-specified transition between stages. This integration of phases may enhance efficiency. Methods: To understand the precedent for the use of seamless designs, this working group was formed to conduct a comprehensive literature search on seamless clinical trials conducted with confirmatory intent in oncology. Trial design features were extracted into a database and analyzed with descriptive statistics. Results: A literature search identified 68 clinical trials meeting inclusion and exclusion criteria. The most common design feature was a gate on treatment efficacy, where the trial would only proceed to the second stage if sufficient efficacy was observed in the first. The next most common feature was a selection of a dose or treatment regimen. Inferentially and operationally seamless designs were approximately equally represented. Discussion: Key statistical considerations for seamless phase II/III designs include optimizing design choices by evaluating and comparing operating characteristics across design alternatives as well as showing control of overall Type I error rates. Executing the transition between phases should be evaluated for issues related to accrual, drug production, and procedures to maintain trial integrity. Conclusions: While there are unique statistical, regulatory, and operational considerations for seamless designs they are also uniquely suited to many development settings. These include, for example, addressing dose selection under FDA's Project Optimus and addressing the growing use of biomarkers and personalized medicine approaches in cancer treatment. [ABSTRACT FROM AUTHOR]

Copyright of Osmangazi Journal of Medicine / Osmangazi Tip Dergisi is the property of Eskisehir Osmangazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Yang Yang,1 Jin Shang,2 Shuyun Xu,2 Zhen Wang1 1Department of Pharmacy, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 2Department of Respiratory and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaCorrespondence: Zhen Wang, Email zhencp303@qq.comBackground: Pulmonary cryptococcosis is a fungal infection of the lungs, particularly challenging to treat in patients with multiple comorbidities such as obesity, type 2 diabetes, and cirrhosis. Fluconazole is a first-line medication for the treatment of pulmonary cryptococcosis, but currently there is a lack of clinical medication experience in obese patients with multiple comorbidities, especially in dose adjustment after treatment failure.Case Introduction: This case report describes the experience of fluconazole in the treatment of pulmonary cryptococcal infection in a 45-year-old Chinese male with obesity, type 2 diabetes, and cirrhosis. The patient had a history of antifungal therapy for two weeks before admission, but the cough and hemoptysis were not improved. The treatment failed. After admission, it was recommended to use a conventional dose of fluconazole as an antifungal regimen according to the guidelines. However, the treatment effect was still unsatisfactory, due to the patients’ cough, hemoptysis, and fever symptoms were not relieved. During this period, it was newly found that the patient had cirrhosis and type 2 diabetes and had not previously controlled blood glucose. Considering the above situation, combined with the pharmacokinetic characteristics of fluconazole and the patient’s weight reaching 113 kg, the team readjusted the fluconazole medication regimen, and ultimately, the pulmonary infection improved without significant adverse reactions.Results: We found that it was more suitable for patients with obesity to calculate the dose of fluconazole by the lean weight. By estimation, the patient was finally given a loading dose of 800 mg fluconazole, and his condition improved significantly. After two weeks of medication, it was adjusted to a maintenance dose of 600 mg until the pulmonary infection in the patient disappeared.Conclusion: This case suggests that fluconazole antifungal therapy for pulmonary cryptococcal infection should fully consider the risk of comorbidities in patients. If necessary, medication dosage can be adjusted according to weight, and it is recommended to use lean bodyweight for evaluation and optimization. In addition, close attention should be paid to liver and kidney function.Keywords: pulmonary cryptococcosis, fluconazole, antifungal therapy, dose optimization, obese, lean bodyweight

Abstract Background Incorporating backfill cohorts in phase I oncology trials is a recently developed strategy for dose optimization. However, the efficacy assessment window is long in general, causing a lag in identifying ineffective doses and more patients being backfilled to those doses. There is necessity to investigate how to use patient-reported outcomes (PRO) to determine doses for backfill cohorts. Methods We propose a unified Bayesian design framework, called ‘Backfill-QoL’, to utilize patient-reported quality of life (QoL) data into phase I oncology trials with backfill cohorts, including methods for trial monitoring, algorithm for dose-finding, and criteria for dose selection. Simulation studies and sensitivity analyses are conducted to evaluate the proposed Backfill-QoL design. Results The simulation studies demonstrate that the Backfill-QoL design is more efficient than traditional dose-expansion strategy, and fewer patients would be allocated to doses with unacceptable QoL profiles. A user-friendly Windows desktop application is developed and freely available for implementing the proposed design. Conclusions The Backfill-QoL design enables continuous monitoring of safety, efficacy and QoL outcomes, and the recommended phase II dose (RP2D) can be identified in a more patient-centered perspective.

It has become standard in medical treatment to base dosage on evidence in randomized trials. Yet it has been rare to study how outcomes vary with dosage. In trials to obtain drug approval, the norm has been to compare some dose of a new drug with an established therapy or placebo. Standard trial analysis views each trial arm as qualitatively different, but it may be credible to assume that efficacy and adverse effects weakly increase with dosage. Optimization of patient care requires joint attention to both, as well as to treatment cost. This article develops a methodology to use limited trial evidence to choose dosage when efficacy and adverse effects weakly increase with dose. I suppose that dosage is an integer t ∊ (0,1,..., T), T being a specified maximum dose. I study dosage choice when trial evidence on outcomes is available for only K dose levels, where K < T + 1. Then the population distribution of dose response is partially identified. I show that the identification region is a convex polygon. I characterize clinical and population decision-making using the minimax regret criterion. A simple analytical solution exists when T = 2. Computation is tractable when T is larger. [ABSTRACT FROM AUTHOR]

Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal drug doses for any pharmacometrics model for a given dosing scenario. In the present work, we enhance the OptiDose concept to compute optimal drug dosing with respect to both efficacy and safety targets. Usually, these are not of equal importance, but one is a top priority, that needs to be satisfied, whereas the other is a secondary target and should be achieved as good as possible without failing the top priority target. Mathematically, this leads to state-constrained optimal control problems. In this paper, we elaborate how to set up such problems and transform them into classical unconstrained optimal control problems which can be solved in NONMEM. Three different optimal dosing tasks illustrate the impact of the proposed enhanced OptiDose method. [ABSTRACT FROM AUTHOR]

Effective treatment of sepsis not only demands prompt administration of appropriate antimicrobials but also requires precise dosing to enhance the likelihood of patient survival. Adequate dosing refers to the administration of doses that yield therapeutic drug concentrations at the infection site. This ensures a favorable clinical and microbiological response while avoiding antibiotic-related toxicity. Therapeutic drug monitoring (TDM) is the recommended approach for attaining these goals. However, TDM is not universally available in all intensive care units (ICUs) and for all antimicrobial agents. In the absence of TDM, healthcare practitioners need to rely on several factors to make informed dosing decisions. These include the patient's clinical condition, causative pathogen, impact of organ dysfunction (requiring extracorporeal therapies), and physicochemical properties of the antimicrobials. In this context, the pharmacokinetics of antimicrobials vary considerably between different critically ill patients and within the same patient over the course of ICU stay. This variability underscores the need for individualized dosing. This review aimed to describe the main pathophysiological changes observed in critically ill patients and their impact on antimicrobial drug dosing decisions. It also aimed to provide essential practical recommendations that may aid clinicians in optimizing antimicrobial therapy among critically ill patients.

Abstract 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.

Genetic diversity enhancement to select Bambara groundnut (Vigna subterranea [L.] Verdc. 2x = 2n = 22) breeding lines with economic traits is imperative to improve the crop's utilization in Sub-Saharan Africa. Gamma radiation is a vital mutagenic agent to create novel allelic combinations required for developing climate-smart Bambara groundnut varieties to contribute to food production and nutrition security in drier regions, including Namibia. Therefore, the objective of the present study was to identify optimal gamma radiation doses to enhance genetic diversity in Bambara groundnut genotypes. The present study was conducted in root boxes under a custom-made shade-net house at Mannheim Crop Research Station, Tsumeb, Namibia. Three Bambara groundnut genotypes (Uniswa Red, Dip C and KFBN 9709) were gamma irradiated at six doses (0, 50, 100, 150, 200, and 250 Gy) were laid out in a randomized complete block design (RCBD) with three replications. Data were collected on days to emergence (DTE), emergence percentage (%EM), seedling survival percentage (%SS), seedling length (SLT), and shoot weight (STW). A non-significant interaction of genotype × gamma radiation dose was detected, showing stable responses indicating that genotypes require varying gamma radiation doses for mutagenesis. A significant (p

Loratadine is metabolized to desloratadine. Both of them have been used for allergy treatment in children. Anatomical, physiological, and biological parameters of children and clearance of drugs vary with age. We aimed to develop a whole‐body physiologically based pharmacokinetic (PBPK) model to simultaneously predict the pharmacokinetics of loratadine and desloratadine in children. Following validation using 11 adult data sets, the developed PBPK model was extrapolated to children. Plasma concentrations following oral loratadine or desloratadine to children of different ages were simulated and compared with six children data sets. After scaling anatomy/physiology, protein binding, and clearance, pharmacokinetics of the two drugs in pediatric populations were satisfactorily predicted. Most of the observed concentrations fell within the 5th‐95th percentile range of the simulations in 1000 virtual children. The predicted area under the concentration‐time curve (AUC) and Cmax fell within 0.5‐2.0‐fold range of the observations. Oral doses of loratadine or desloratadine for children of different ages were simulated based on similar AUCs following 10 mg of loratadine or 5 mg of desloratadine for adults. Pediatric PBPK model was successfully developed to simultaneously predict plasma concentrations of loratadine and desloratadine in children of all ages. The developed pediatric PBPK model may also be applied to optimize pediatric dosage. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: Uterine artery embolization is performed in pre‐menopausal women. Understanding the contribution of radiation dose at each stage of the procedure is important for potential dose reduction. The aim was to retrospectively analyse radiation dose on a per‐procedural‐stage basis, comparing digital subtraction angiography (DSA) and conventional roadmap (CRM). Methods: Group A consisted of 50 patients where DSA was used for road mapping at all stages: (I) Aortogram, (II) Left internal iliac artery (IIA) DSA, (III) Left uterine artery (UA) DSA, (IV) Right IIA DSA and (V) Right UA DSA. Group B included 50 patients, where CRM was used for road mapping at stages (II) and (IV). Results: For Group A, mean total dose‐area product (DAP) was 39.7 Gy·cm2; mean DAP for each stage were (I) Aortogram = 3.4 Gy·cm2, (II) Left IIA DSA = 5.9 Gy·cm2, (III) Left UA DSA = 3.2 Gy·cm2, (IV) Right IIA DSA = 5.5 Gy·cm2 and (V) Right UA DSA = 3.0 Gy·cm2. For Group B, mean total DAP was 33.6 Gy·cm2, mean DAP for each stage were (I) Aortogram = 3.3 Gy·cm2, (II) Left IIA CRM = 1.5 Gy·cm2, (III) Left UA DSA = 3.3 Gy·cm2, (IV) Right IIA CRM = 1.5 Gy·cm2 and (V) Right UA DSA = 3.3 Gy·cm2. Fluoroscopy time was 10 and 9.4 min for Groups A and B, respectively. Conclusion: The highest road‐mapping radiation dose contribution was from bilateral IIA DSA. The use of CRM, intermittent fluoroscopy and elimination of the aortogram is recommended to further reduce procedural radiation dose. [ABSTRACT FROM AUTHOR]

Dose optimization in computed tomography (CT) is crucial, especially in CT fluoroscopy (fluoro-CT) used for real-time navigation, affecting both patient and operator safety. This study evaluated the impact of spectral X-ray filtering using a tin filter (Sn filter), and a method called partial-angle computed tomography (PACT), which involves segmentally switching off the X-ray tube current at the ambient dose rate H ˙ * (10) at the interventional radiologist's (IR) position. Measurements were taken at two body regions (upper body: head/neck; lower body: lower legs/feet) using a 120 kV X-ray tube voltage, 3 × 5.0 mm CT collimation, 0.5 s rotation speed, and X-ray tube currents of 43 Eff.mAs (without Sn filter) and 165 Eff.mAs (with Sn filter). The study found significant dose reductions in both body regions when using the Sn filter and PACT together. For instance, in the upper body region, the combination protocol reduced H ˙ * (10) from 11.8 µSv/s to 6.1 µSv/s (p < 0.0001) compared to the protocol without using these features. Around 8% of the reduction (about 0.5 µSv/s) is attributed to the Sn filter (p = 0.0005). This approach demonstrates that using the Sn filter along with PACT effectively minimizes radiation exposure for the IR, particularly protecting areas like the head/neck, which can only be insufficiently covered by (standard) radiation protection material. [ABSTRACT FROM AUTHOR]

Technological differences between computed radiography (CR) and digital radiography (DR) systems can influence patient doses and exposure parameters in pelvic x-ray examinations. The presence of radiosensitive organs in the pelvic region underscores the need to optimize these parameters for both CR and DR systems. This prospective study aimed to compare the patient doses and exposure parameters for adult patients undergoing pelvic x-ray examinations using CR and DR systems, based on data from Sri Lanka. The study included data from 56 x-ray examinations, with 25 using CR and 31 using DR. Patient demographic characteristics and exposure parameters (kVp: kilovoltage peak, mAs: tube current-exposure time product) were recorded, and patient doses were measured in terms of the kerma-area product (PKA) using a PKA meter. Despite similar mean weight and body mass index (BMI), the CR systems showed significantly higher mean kVp (7.4%), mAs (16.4%), and PKA (29.7%) than the DR systems (CR - kVp: 73.2, mAs: 37.8, PKA: 2.29 Gy cm2; DR - kVp: 67.8, mAs: 31.6, PKA: 1.61 Gy cm2). The Mann-Whitney U test revealed statistically significant differences in PKA and kVp between the CR and DR systems (p < 0.05). Furthermore, even with lower patient weight and BMI, the mean mAs and PKA in this study were substantially higher than those reported in the literature for both CR and DR systems. These results suggest the need to optimize current mAs settings for the studied hospitals and introduce radiographic system-specific exposure parameters and reference dose levels for pelvic x-ray examinations in order to enhance patient protection. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: 5-Fluorouracil (5-FU) is a chemotherapy drug commonly used in the treatment of colon cancer. There is a significant drive to mitigate the limitations of 5-fluorouracil while maintaining its therapeutic potency by exploring the potential of using natural products such as curcumin in conjunction with 5-FU. Thus, the present study investigates the in vitro pharmacodynamic interaction between 5-FU and curcumin using the Chou-Talalay method in HT29 cell lines. Materials and Methods: Initially, the estimation of half-maximal concentration of the medications against HCT116 and HT29 cell lines was performed by 3-4,5-dimethylthiazol-2-yl-2,5 diphenyl tetrazolium bromide assay. Later, the combination effect was studied in HT29 cell lines by constant ratio experiment design. Results and Discussion: The research findings revealed that the 5-FU and curcumin exhibit dose-dependent anti-proliferative activity in HCT116 and HT29 cells. Moreover, it was observed that HT29 cell lines are less sensitive to 5-FU than HCT116 cell lines, rather than curcumin was found to inhibit cell proliferation in both cell lines with no significant statistical difference (p<0.05). The Combination Index (CI) values of less than 1 for all the combined experimental ratios indicated the synergistic effect of the two compounds. The generated Dose Reduction Index (DRI) values for the combination imply for the favourable dose reduction in the treatment of colorectal cancer. Conclusion: Significantly, the study indicates that 40:1 ratio of 5-FU to curcumin might represent an ideal proportion for co-administration using an appropriate delivery system. [ABSTRACT FROM AUTHOR]

For novel immuno‐oncology therapies, the primary purpose of a dose‐finding trial is to identify an optimal dose (OD), defined as the tolerable dose having adequate efficacy and immune response under the unpredictable dose–outcome (toxicity, efficacy, and immune response) relationships. In addition, the multiple low or moderate‐grade toxicities rather than dose‐limiting toxicities (DLTs) and multiple levels of efficacy should be evaluated differently in dose‐finding to determine true OD for developing novel immuno‐oncology therapies. We proposed a generalized Bayesian optimal interval design for immunotherapy, simultaneously considering efficacy and toxicity grades and immune response outcomes. The proposed design, named gBOIN‐ETI design, is model‐assisted and easy to implement to develop immunotherapy efficiently. The operating characteristics of the gBOIN‐ETI are compared with other dose‐finding trial designs in oncology by simulation across various realistic settings. Our simulations show that the gBOIN‐ETI design could outperform the other available approaches in terms of both the percentage of correct OD selection and the average number of patients allocated to the OD across various realistic trial settings. [ABSTRACT FROM AUTHOR]

Over the past decade, drug development in oncology has shifted from cytotoxic agents to drugs with new mechanisms of action, such as cancer immunotherapies, targeted therapeutics, T-cell engagers and others. The conventional maximum tolerated dose (MTD) based dose-finding paradigm is not suitable for the development of these new agents. Further, health authorities, especially the FDA, are requesting more thorough dose optimization prior to the initiation of pivotal trials, and initiatives such as the FDA's project Optimus have been launched to accelerate this paradigm shift. Dose optimization is more complicated than finding the MTD and requires consideration of complex mechanisms of action, schedule optimization, long-term drug tolerability, and possibly novel pharmacodynamic endpoints. Thus, thoughtful study designs, translational data, and statistical modeling play an increasingly important role to achieve the goal of dose optimization. This article captures opinions from the 2022 ASA biophamaceutical section regulatory-industry statistics workshop session "Dose finding and optimization for novel oncology agents—the new challenges and novel technologies." We present general design options for dose optimization. Pros and cons of these design options are discussed, and a real-world case study is provided to illustrate a strategy of dose optimization. Discussions focused on practical considerations are included. [ABSTRACT FROM AUTHOR]

The traditional MTD-based dose selection paradigm commonly used for cytotoxic chemotherapies might not be optimal for targeted therapies because a higher dose does not necessarily result in improved anti-cancer activity. With the initiation of "Project Optimus" at the FDA, a randomized dose optimization study at the early stage of drug development has become necessary for oncology drug development. We propose a Bayesian Dose Optimization Design for Randomized Phase II trials (DODII) that integrates Bayesian continuous monitoring and Bayesian pick-the-winner approach in a randomized design, where efficacy and toxicity endpoints are jointly used to inform the dose selection. The adaptive feature of the DODII design will terminate a suboptimal dose at an interim analysis under two circumstances: (a) when efficacy or safety data is unpromising, (b) when the other dose option is clearly better. The critical values of the decision rule for the DODII design can be enumerated prior to the initiation of the clinical study, which makes it practically easy to implement. Through simulation studies, we showed that the DODII design has favorable operating characteristics with controlled selection error, higher power to select the optimal dose option, and reduced total sample size as compared to the other commonly used methods. [ABSTRACT FROM AUTHOR]

Purpose: The number of patients with bariatric surgery who receive oral anticancer drugs is rising. Bariatric surgery may affect the absorption of oral anticancer drugs. Strikingly, no specific drug dosing recommendations are available. We aim to provide practical recommendations on the application of oral anticancer drugs in patients who underwent bariatric surgery. Methods: Patients with any kind of bariatric surgery were extracted retrospectively in a comprehensive cancer center. In addition, a flowchart was proposed to assess the risk of inadequate exposure to oral anticancer drugs in patients who underwent bariatric surgery. Subsequently, the flowchart was evaluated retrospectively using routine Therapeutic drug monitoring (TDM) samples. Results: In our analysis, 571 cancer patients (0.4% of 140.000 treated or referred patients) had previous bariatric surgery. Of these patients, 78 unique patients received 152 oral anticancer drugs equaling an overall number of 30 unique drugs. The 30 different prescribed oral anticancer drugs were categorized as low risk (13%), medium risk (67%), and high risk (20%) of underdosing. TDM plasma samples of 25 patients (82 samples) were available, of which 21 samples post-bariatric surgery (25%) were below the target value. Conclusions: The proposed flowchart can support optimizing the treatment with orally administered anticancer drugs in patients who underwent bariatric surgery. We recommend performing TDM in drugs that belong to BCS classes II, III, or IV. If more risk factors are present in BCS classes II or IV, a priori switches to other drugs may be advised. In specific cases, higher dosages can be provided from the start (e.g., tamoxifen). [ABSTRACT FROM AUTHOR]

AbstractDrug combinations are increasingly used in cancer treatment to enhance drug effectiveness through synergistic therapeutic effects. However, determining the optimal biological dose combination (OBDC) in small-scale drug combination trials presents challenges due to the increased complexity of the dose space. To effectively optimize the dose combination of combined drugs, we propose a model-assisted design by extending the single-agent Bayesian optimal interval phase I/II (BOIN12) design. Our approach incorporates a utility function to balance the tradeoff between risk and benefit and directly models the utility of each dose by constructing a quasi-beta-binomial model. A key advantage of our design is the simplification of decision-making during interim periods by considering all possible outcomes and pre-including the decision rule in the protocol. Additionally, we present a time-to-event (TITE) version of our design, employing an approximate likelihood approach to mitigate potential late-onset effects. We demonstrate that our proposed design exhibits robust and desirable operating characteristics across various scenarios through extensive simulation studies. [ABSTRACT FROM AUTHOR]

Simple Summary: The majority of oncology drugs approved by the Food and Drug Administration over the past quarter century have been targeted therapies, designed to act on cancer cells with specific molecular abnormalities. Although these newer agents have different properties compared to cytotoxic chemotherapy, the same methodology used for dose-finding during drug development has continued to be used. Growing awareness of the inadequacy of traditional dose selection processes for modern cancer drugs has been the catalyst for the creation of new regulatory guidance aimed at reforming current practices. This review summarizes six cases, illustrating different approaches to targeted therapy dose optimization. Therapeutic advances in oncology in the 21st century have contributed to significant declines in cancer mortality. Notably, targeted therapies comprised the largest proportion of oncology drugs approved by the United States (US) Food and Drug Administration (FDA) over the past 25 years and have become the standard of care for the treatment of many cancers. However, despite the metamorphosis of the therapeutic landscape, some aspects of cancer drug development have remained essentially unchanged. In particular, the dose-finding methodology originally developed for cytotoxic chemotherapy drugs continues to be implemented, even though this approach no longer represents the most appropriate strategy for modern cancer therapies. In recognition of the need to reconsider assumptions, adapt the dose selection process for newer drugs, and design alternative strategies, the FDA has undertaken several initiatives in recent years to address these concerns. These actions include the launch of Project Optimus in 2021 and the issuance of draft guidance for industry on dose optimization of oncology drugs in 2023. Amid this evolving regulatory environment, the present manuscript reviews case studies for six different targeted cancer therapies, highlighting how dose-finding challenges have been managed to date by oncologists, sponsors, and regulators. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

AbstractThe advent of dose optimization has led to a paradigm shift in oncology clinical trials to find the optimal biologic dose (OBD). Phase 1/2 trials with randomized doses can facilitate additional investigation of the identified OBD in a targeted population by incorporating safety and efficacy data. We propose to compare randomized doses by extending the Bayesian optimal interval phase 1/2 (BOIN12) approach, an OBD-finding design in which a utility is used to account for risk-benefit tradeoffs. Specifically, we used the BOIN12 standardized mean utilities in a hypothesis testing framework, in which frequentist and Bayesian inference were evaluated to compare the standardized mean utilities across doses and identify the OBD. We performed simulation studies and a hypothetical oncology study to compare the proposed BOIN12 utility-based extension (U-MET) design with an empirical design. The results demonstrated that the U-MET design has satisfactory operating characteristics for selecting the OBD. We recommend using the U-MET design as the primary dose comparison approach, or alternatively as supportive evidence, for optimal dose selection. [ABSTRACT FROM AUTHOR]

Genetic diversity enhancement to select Bambara groundnut (Vigna subterranea [L.] Verdc. 2x = 2n = 22) breeding lines with economic traits is imperative to improve the crop's utilization in Sub-Saharan Africa. Gamma radiation is a vital mutagenic agent to create novel allelic combinations required for developing climate-smart Bambara groundnut varieties to contribute to food production and nutrition security in drier regions, including Namibia. Therefore, the objective of the present study was to identify optimal gamma radiation doses to enhance genetic diversity in Bambara groundnut genotypes. The present study was conducted in root boxes under a custom-made shade-net house at Mannheim Crop Research Station, Tsumeb, Namibia. Three Bambara groundnut genotypes (Uniswa Red, Dip C and KFBN 9709) were gamma irradiated at six doses (0, 50, 100, 150, 200, and 250 Gy) were laid out in a randomized complete block design (RCBD) with three replications. Data were collected on days to emergence (DTE), emergence percentage (%EM), seedling survival percentage (%SS), seedling length (SLT), and shoot weight (STW). A non-significant interaction of genotype x gamma radiation dose was detected, showing stable responses indicating that genotypes require varying gamma radiation doses for mutagenesis. A significant (p < 0.01) genotype effect was found on days to emergence, emergence percentage, seedling length and shoot weight, indicating that gamma radiation doses requirements for individual genotypes varied. Significant (p < 0.05) effects of gamma radiation doses revealed that the traits could be used to select optimum doses to be upscale in induced mutation breeding programs of Bambara groundnut. Seedling length was the only trait with higher growth reduction that resulted in a negative trends. A linear regression model predicted LD50s for Uniswa Red, Dip C and KBFN 9709 at 428.1, 523.7 and 712.5 GY, respectively. These doses were higher than test gamma radiation doses, indicating the need for higher doses resulting in LD50s. Therefore, the doses identified in these studies are useful for use in induced mutation breeding for the tested Bambara groundnut genotypes. [ABSTRACT FROM AUTHOR]

Interstitial high-dose-rate brachytherapy (HDR-BT) is an effective therapy modality for patients with localized prostate carcinoma. The objectives of the study were to optimise the therapy regime variables using two models: response surface methodology (RSM) and artificial neural network (ANN). Thirty-one studies with 5651 patients were included (2078 patients presented as low-risk, 3077 patients with intermediate-risk, and 496 patients with high-risk). A comparison of these therapy schedules was carried out using an effective biologically effective dose (BED ef) that was calculated assuming the number of treatment days and dose (D) per day. The modelling and optimization of therapy parameters (BED ef and risk level) in order to obtain the maximum biochemical free survival (BFS) were carried out by the RSM and ANN models. An optimal treatment schedule (BFS = 97%) for patients presented with low-risk biochemical recurrence would be D = 26 Gy applied in one application, 2 fractions at least 6 h apart, within an overall treatment time of 1 day (BED ef = 251 Gy) by the RSM and ANN model. For patients presented with intermediate- or high-risk an optimal treatment regime (BFS = 94% and 90%, respectively) would be D = 38 Gy applied in one application, 4 fractions at least 6 h apart, with an overall treatment time of 2 days (BED ef = 279 Gy) by the RSM and ANN models. The RSM and ANN models determine almost the same optimal values for the set of predicted therapy parameters that make a feasible selection of an optimal treatment regime. • HDR-BT is an effective therapy modality for patients with localised prostate carcinoma. • Optimisation of HDR-BT regime variables was carried out by the RSM and ANN models. • The RSM and ANN models determined almost the same optimal values. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

ObjectiveThe aim of this study was to develop a population pharmacokinetic model for ceftazidime in critically ill children in the pediatric intensive care unit (PICU) and optimize an appropriate dosing regimen for this population.MethodsWe performed a prospective pharmacokinetic study on critically ill children aged 0.03–15 years. A population pharmacokinetic model was developed using the NLME program. Statistical and graphical methods were used to assess the stability and predictive performance of the model. Monte Carlo simulations were conducted to determine the optimal ceftazidime dosing regimen to achieve 70% fT > minimum inhibitory concentration (MIC).ResultsThis study included 88 critically ill children and 100 ceftazidime serum concentrations. The pharmacokinetic characteristics of ceftazidime were best described by a one-compartment linear elimination model. The weight and estimated glomerular filtration rate (eGFR) were determinant covariates for the clearance (CL) of ceftazidime. The recommended ceftazidime dosage regimens achieved a probability of target attainment (PTA) >90% for critically ill children at MIC values of 2, 4, and 8 mg/L. For bacterial infection at an MIC of 16 mg/L, it is difficult to achieve effective pharmacodynamic (PD) targets in vivo with the commonly used dose of ceftazidime.ConclusionThe population pharmacokinetic model of ceftazidime was established in critically ill children. Based on this model, we recommend evidence-based, individualized dosing regimens for subgroups with different weights and renal functions. The current daily dosage for children adequately meets the treatment requirements for MICs of 2, 4, and 8 mg/L, while for bacterial infection at an MIC of 16 mg/L, an elevated dosage regimen may be required.Clinical Trial Registrationhttps://www.medicalresearch.org.cn/login, Identifier MR-42-22-000220.

There is presently no efficient dose individualization strategy for the use of antiseizure medications in epileptic pregnant patients. This study aimed to develop a population pharmacokinetics model for levetiracetam and propose a tailored adaptive individualized dosage strategy for epileptic pregnant patients. A total of 322 levetiracetam plasma concentrations from 238 patients with epilepsy were included, including 216 women with epilepsy (20.83% of whom were pregnant). The levetiracetam plasma concentration was measured using a validated ultra-performance liquid chromatography-tandem mass spectrometry assay, and the data were modeled using a nonlinear mixed-effects model. The resultant model served as the basis for simulating the dosage adjustment strategy. A one-compartment model with first-order elimination best described the pharmacokinetic data of levetiracetam. The apparent clearance (CL/F) was 3.43 L/h (95% CI 3.30–3.56) and the apparent volume of distribution was 43.7 L (95% CI 40.4–47.0) for a typical individual of 57.2 kg. Pregnancy and body weight were found to be significant covariates of CL/F of levetiracetam. The recommended regimen of levetiracetam could be predicted by the population pharmacokinetic model based on body weight, gestational age, and the daily dose of levetiracetam taken before pregnancy. [ABSTRACT FROM AUTHOR]