BACKGROUND: Due to the narrow therapeutic window and large pharmacokinetic variation of valproic acid (VPA), it is difficult to make an optimal dosage regimen. The present study aims to optimize the initial dosage of VPA in patients with bipolar disorder.
METHODS: A total of 126 patients with bipolar disorder treated by VPA were included to construct the VPA population pharmacokinetic model retrospectively. Sex differences and combined use of clozapine were found to significantly affect VPA clearance in patients with bipolar disorder. The initial dosage of VPA was further optimized in male patients without the combined use of clozapine, female patients without the combined use of clozapine, male patients with the combined use of clozapine, and female patients with the combined use of clozapine, respectively.
RESULTS: The CL/F and V/F of VPA in patients with bipolar disorder were 11.3 L/h and 36.4 L, respectively. It was found that sex differences and combined use of clozapine significantly affected VPA clearance in patients with bipolar disorder. At the same weight, the VPA clearance rates were 1.134, 1, 1.276884, and 1.126 in male patients without the combined use of clozapine, female patients without the combined use of clozapine, male patients with the combined use of clozapine, and female patients with the combined use of clozapine, respectively. This study further optimized the initial dosage of VPA in male patients without the combined use of clozapine, female patients without the combined use of clozapine, male patients with the combined use of clozapine, and female patients with the combined use of clozapine, respectively.
CONCLUSIONS: This study is the first to investigate the initial dosage optimization of VPA in patients with bipolar disorder based on sex differences and the combined use of clozapine. Male patients had higher clearance, and the recommended initial dose decreased with increasing weight, providing a reference for the precision drug use of VPA in clinical patients with bipolar disorder.

Primary membranous nephropathy (PMN) is the most common cause for adult nephrotic syndrome. Rituximab has demonstrated promising clinical efficacy by random controlled trials and the off-label use is widely adopted in PMN. However, the standard dosage is borrowed from B cell lymphoma treatment with far more antigens and is oversaturated for PMN treatment, accompanied with additional safety risk and unnecessary medical cost. More than 15% serious adverse events were observed under standard dosage and low dose therapies were explored recently. Dose optimization by clinical trials is extremely time- and cost-consuming and can be significantly accelerated with the aid of model-informed drug development. Here, we aim to establish the first population pharmacokinetic and pharmacodynamic (PPK/PD) model for rituximab in PMN to guide its dosage optimization. Rituximab pharmacokinetic and pharmacodynamic data from 41 PMN patients in a retrospective study under a newly proposed monthly mini-dose were used to construct quantitative dose-exposure-response relationship via mechanistic target-mediated drug disposition (TMDD) model followed by regression between the reduction of anti-PLA2R titer and time after the treatment. The final model, validated by goodness-of-fit plots, visual predictive checks and bootstrap, was used to recommend the optimized dosing regimen by simulations. The model was well validated for PK/PD prediction. The systemic clearance and half-life are 0.54 L/h and 14.7 days, respectively. Simulation of a novel regimen (6 monthly doses of 100 mg) indicated the comparable ability and superior duration time of CD20+ B cell depletion compared with standard dosage, while the cumulative dosage and safety risk was significantly decreased. We established the first PPK/PD model and provide evidence to support the dosage optimization based on monthly mini-dose. Our study can also efficiently accelerate dosage optimization of novel anti-CD20 antibodies in PMN and other indications.

Drug resistance is a significant obstacle to effective cancer treatment. To gain insights into how drug resistance develops, we adopted a concept called fitness landscape and employed a phenotype-structured population model by fitting to a set of experimental data on a drug used for ovarian cancer, olaparib. Our modeling approach allowed us to understand how a drug affects the fitness landscape and track the evolution of a population of cancer cells structured with a spectrum of drug resistance. We also incorporated pharmacokinetic (PK) modeling to identify the optimal dosages of the drug that could lead to long-term tumor reduction. We derived a formula that indicates that maximizing variation in plasma drug concentration over a dosing interval could be important in reducing drug resistance. Our findings suggest that it may be possible to achieve better treatment outcomes with a drug dose lower than the levels recommended by the drug label. Acknowledging the current limitations of our work, we believe that our approach, which combines modeling of both PK and drug resistance evolution, could contribute to a new direction for better designing drug treatment regimens to improve cancer treatment.

Oncology drug discovery and development has always been an area facing many challenges. Phase 1 oncology studies are typically small, open-label, sequential studies enrolling a small sample of adult patients (i.e., 3-6 patients/cohort) in dose escalation. Pediatric evaluations typically lag behind the adult development program. The pediatric starting dose is traditionally referenced on the recommended phase 2 dose in adults with the incorporation of body size scaling. The size of the study is also small and dependent upon the prevalence of the disease in the pediatric population. Similar to adult development, the dose is escalated or de-escalated until reaching the maximum tolerated dose (MTD) that also provides desired biological activities or efficacy. The escalation steps and identification of MTD are often rule-based and do not incorporate all the available information, such as pharmacokinetic (PK), pharmacodynamic (PD), tolerability and efficacy data. Therefore, it is doubtful if the MTD approach is optimal to determine the dosage. Hence, it is important to evaluate whether there is an optimal dosage below the MTD, especially considering the emerging complexity of combination therapies and the long-term tolerability and safety of the treatments. Identification of an optimal dosage is also vital not only for adult patients but for pediatric populations as well. Dosage-finding is much more challenging for pediatric populations due to the limited patient population and differences among the pediatric age range in terms of maturation and ontogeny that could impact PK. Many sponsors defer the pediatric strategy as they are often perplexed by the challenges presented by pediatric oncology drug development (model of action relevancy to pediatric population, budget, timeline and regulatory requirements). This leads to a limited number of approved drugs for pediatric oncology patients. This review article provides the current regulatory landscape, incentives and how they impact pediatric drug discovery and development. We also consider different pediatric cancers and potential clinical trial challenges/opportunities when designing pediatric clinical trials. An outline of how quantitative methods such as pharmacometrics/modelling & simulation can support the dosage-finding and justification is also included. Finally, we provide some reflections that we consider helpful to accelerate pediatric drug discovery and development.

Due to the small sample sizes in early-phase clinical trials, the toxicity and efficacy profiles of the dose-schedule regimens determined for subsequent trials may not be well established. The recent development of novel anti-tumor treatments and combination therapies further complicates the problem. Therefore, there is an increasing recognition of the essential place of optimizing dose-schedule regimens, and new strategies are now urgently needed. Bayesian adaptive designs provide a potentially effective way to evaluate several doses and schedules simultaneously in a single clinical trial with higher efficiency, but real-world implementation examples of such adaptive designs are still few. In this paper, we cover the critical factors associated with dose-schedule optimization and review the related innovative Bayesian adaptive designs. The assumptions, characteristics, limitations, and application scenarios of those designs are introduced. The review also summarizes some unresolved issues and future research opportunities for dose-schedule optimization.

BACKGROUND: This proof-of-concept retrospective case study investigated whether patient-reported outcomes (PRO) instruments, designed to capture symptomatic adverse event data, could identity a known exposure-response (ER) relationship for safety characterized in an original FDA analysis of an approved anti-cancer agent. PRO instruments have been designed to uniquely quantify the tolerability aspects of exposure-associated symptomatic adverse events. We explored whether standard ER analyses of clinician-reported safety data for symptomatic adverse events could be complemented by ER analysis using PRO data that capture and quantify the tolerability aspects of these same symptomatic adverse events.
METHODS: Exposure-associated adverse event data for diarrhea were analyzed in parallel in 120 patients enrolled in a clinical trial using physician reported Common Terminology Criteria for Adverse Events (CTCAE) and patient-reported symptomatic adverse event data captured by the National Cancer Institute\'s (NCI) PRO Common Terminology Criteria for Adverse Events (PRO-CTCAE) instrument. Comparative ER analyses of diarrhea were conducted using the same dataset. Results from the CTCAE and PRO-CTCAE ER analyses were assessed for consistency with the ER relationship for diarrhea established in the original NDA using a 750-patient dataset. The analysis was limited to the 120-patient subset with parallel CTCAE and PRO-CTCAE assessments.
RESULTS: Within the same 120-patient dataset, ER analysis using dense, longitudinal PRO-CTCAE-derived data was sensitive to identify the known ER relationship for diarrhea, whereas the standard CTCAE based ER analysis was not.
CONCLUSIONS: ER analysis using PRO assessed symptomatic adverse event data may be a sensitive tool to complement traditional ER analysis. Improved identification of relationships for safety, by including quantification of the tolerability aspect of symptomatic adverse events using PRO instruments, may be useful to improve the sensitivity of exposure response analysis to support early clinical trial dosage optimization strategies, where decision making occurs within limited small patient datasets.

OBJECTIVE: The purpose of this study was to optimize radiopharmaceutical dosage in single-photon emission computed tomography (SPECT) nuclear medicine. Therefore, we examined a variable dose (VD) method using body weight as an index in resting myocardial scintigraphy using a 99mTechnetium (99mTc) preparation.
METHODS: In this study, we compared the VD method with the fixed dose (FD) method without a variable by body weight. There were 50 patients using the VD method and 50 patients using the FD method. For the VD method, we set the target average count (counts/pixel) per SPECT view. Using the myocardial average count of the FD method, and the estimated intracorporeal radioactivity at the start of the examination, the dose of the VD method, which varies appropriately depending on the body weight, was calculated.
RESULTS: The VD method had less variation in myocardial counting and was closer to the target count than the FD method, and the median dosage decreased.
CONCLUSIONS: The VD method suggested the possibility of obtaining a count independent of physique and stable image quality, reducing medical and occupational radiation exposure in resting myocardial blood flow scintigraphy.

Formulating a therapeutic strategy that can effectively combat concurrent infections of Actinobacillus pleuropneumoniae (A. pleuropneumoniae) and Pasteurella multocida (P. multocida) can be challenging. This study aimed to 1) establish minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), time kill curve, and post-antibiotic effect (PAE) of tylosin against A. pleuropneumoniae and P. multocida pig isolates and employ the MIC data for the development of epidemiological cutoff (ECOFF) values; 2) estimate the pharmacokinetics (PKs) of tylosin following its intramuscular (IM) administration (20 mg/kg) in healthy and infected pigs; and 3) establish a PK-pharmacodynamic (PD) integrated model and predict optimal dosing regimens and PK/PD cutoff values for tylosin in healthy and infected pigs. The MIC of tylosin against both 89 and 363 isolates of A. pleuropneumoniae and P. multocida strains spread widely, ranging from 1 to 256 μg/mL and from 0.5 to 128 μg/mL, respectively. According to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) ECOFFinder analysis ECOFF value (≤64 µg/mL), 97.75% (87 strains) of the A. pleuropnumoniae isolates were wild-type, whereas with the same ECOFF value (≤64 µg/mL), 99.72% (363 strains) of the P. multicoda isolates were considered wild-type to tylosin. Area under the concentration time curve (AUC), T1/2, and Cmax values were significantly greater in healthy pigs than those in infected pigs (13.33 h × μg/mL, 1.99 h, and 5.79 μg/mL vs. 10.46 h × μg/mL, 1.83 h, and 3.59 μg/mL, respectively) (p < 0.05). In healthy pigs, AUC24 h/MIC values for the bacteriostatic activity were 0.98 and 1.10 h; for the bactericidal activity, AUC24 h/MIC values were 1.97 and 1.99 h for A. pleuropneumoniae and P. multocida, respectively. In infected pigs, AUC24 h/MIC values for the bacteriostatic activity were 1.03 and 1.12 h; for bactericidal activity, AUC24 h/MIC values were 2.54 and 2.36 h for A. pleuropneumoniae and P. multocida, respectively. Monte Carlo simulation lead to a 2 μg/mL calculated PK/PD cutoff. Managing co-infections can present challenges, as it often demands the administration of multiple antibiotics to address diverse pathogens. However, using tylosin, which effectively targets both A. pleuropneumoniae and P. multocida in pigs, may enhance the control of bacterial burden. By employing an optimized dosage of 11.94-15.37 mg/kg and 25.17-27.79 mg/kg of tylosin can result in achieving bacteriostatic and bactericidal effects in 90% of co-infected pigs.

Voriconazole is commonly recommended as a first-line therapy for invasive aspergillosis infections. Elderly patients are susceptible to infectious diseases owing to their decreased physical function and immune system. Our study aims to establish a population pharmacokinetics model for elderly patients receiving intravenous voriconazole, and to optimize dosing protocols through a simulated approach. An accurate fit to the concentration-time profile of voriconazole was achieved by employing a 1-compartment model featuring first-order elimination. The typical clearance rate of voriconazole was found to be 3.22 L/h, with a typical volume of distribution of 194 L. The covariate analysis revealed that albumin (ALB), gamma-glutamyl transpeptidase, and direct bilirubin had significant impacts on voriconazole clearance. Additionally, body weight was found to be associated with the volume of distribution. Individualized dosing regimens were recommended for different ALB levels based on population pharmacokinetics model prediction. The proposed dosing regimens could provide a rationale for dosage individualization, improve the clinical outcomes, and minimize drug-related toxicities.

BACKGROUND: The purpose of the study was to develop a genotype-incorporated population pharmacokinetic (PPK) model of tacrolimus (TAC) in adults with systemic lupus erythematosus (SLE) to investigate the factors influencing TAC pharmacokinetics and to develop an individualized dosing regimen based on the model. In addition, a non-genotype-incorporated model was also established to assess its predictive performance compared to the genotype-incorporated model.
METHODS: A total of 365 trough concentrations from 133 adult SLE patients treated with TAC were collected to develop a genotype-incorporated PPK model and a non-genotype-incorporated PPK model of TAC using a nonlinear mixed-effects model (NONMEM). External validation of the two models was performed using data from an additional 29 patients. Goodness-of-fit diagnostic plots, bootstrap method, and normalized predictive distribution error test were used to validate the predictive performance and stability of the final models. The goodness-of-fit of the two final models was compared using the Akaike information criterion (AIC). The dosing regimen was optimized using Monte Carlo simulations based on the developed optimal model.
RESULTS: The typical value of the apparent clearance (CL/F) of TAC estimated in the final genotype-incorporated model was 14.3 L h-1 with inter-individual variability of 27.6%. CYP3A5 polymorphism and coadministered medication were significant factors affecting TAC-CL/F. CYP3A5 rs776746 GG genotype carriers had only 77.3% of the TAC-CL/F of AA or AG genotype carriers. Omeprazole reduced TAC-CL/F by 3.7 L h-1 when combined with TAC, while TAC-CL/F increased nonlinearly as glucocorticoid dose increased. Similar findings were demonstrated in the non-genotype-incorporated PPK model. Comparing these two models, the genotype-incorporated PPK model was superior to the non-genotype-incorporated PPK model (AIC = 643.19 vs. 657.425). Monte Carlo simulation based on the genotype-incorporated PPK model indicated that CYP3A5 rs776746 AA or AG genotype carriers required a 1/2-1 fold higher dose of TAC than GG genotype carriers to achieve the target concentration. And as the daily dose of prednisone increases, the dose of TAC required to reach the target concentration increases appropriately.
CONCLUSIONS: We developed the first pharmacogenetic-based PPK model of TAC in adult patients with SLE and proposed a dosing regimen based on glucocorticoid dose and CYP3A5 genotype according to the model, which could facilitate individualized dosing for TAC.