Model-informed drug development of autologous CAR-T cell therapy: Strategies to optimize CAR-T cell exposure leveraging cell kinetic/dynamic modeling.

Autologous Chimeric antigen receptor (CAR-T) cell therapy has been highly successful in the treatment of aggressive hematological malignancies and is also being evaluated for the treatment of solid tumors as well as other therapeutic areas. A challenge, however, is that up to 60% of patients do not sustain a long-term response. Low CAR-T cell exposure has been suggested as an underlying factor for a poor prognosis. CAR-T cell therapy is a novel therapeutic modality with unique kinetic and dynamic properties. Importantly, "clear" dose-exposure relationships do not seem to exist for any of the currently approved CAR-T cell products. In other words, dose increases have not led to a commensurate increase in the measurable in vivo frequency of transferred CAR-T cells. Therefore, alternative approaches beyond dose titration are needed to optimize CAR-T cell exposure. In this paper, we provide examples of actionable variables - design elements in CAR-T cell discovery, development, and clinical practice, which can be modified to optimize autologous CAR-T cell exposure. Most of these actionable variables can be assessed throughout the various stages of discovery and development as part of a well-informed research and development program. Model-informed drug development approaches can enable such study and program design choices from discovery through to clinical practice and can be an important contributor to cell therapy effectiveness and efficiency.

Caplacizumab Model-Based Dosing Recommendations in Pediatric Patients With Acquired Thrombotic Thrombocytopenic Purpura.

Acquired thrombotic thrombocytopenic purpura (aTTP) is a rare and life-threatening autoimmune thrombotic microangiopathy. Caplacizumab, evaluated in phase II and III studies in adults, shortens the time to platelet count response and reduces aTTP exacerbations, has a favorable safety profile, and can potentially reduce refractoriness and mortality associated with aTTP. Since no children with aTTP were enrolled in these clinical trials, caplacizumab has been initially approved for use only in adult patients with aTTP (10 mg). Pediatric dosing recommendations were developed using model-based simulations. A semimechanistic pharmacokinetic/pharmacodynamic population model has been developed describing the interaction between caplacizumab and von Willebrand factor antigen (vWF:Ag) following intravenous and subcutaneous administration of caplacizumab in different adult populations, at various dose levels, using nonlinear mixed-effects modeling. Based on the allometrically scaled pharmacokinetic/pharmacodynamic model, different dosing regimens were simulated in 8000 children (aged 2-18 years). Simulated caplacizumab exposures and vWF:Ag levels across different age categories were compared to an adult reference group. A simulated daily dose of 5 mg in children weighing <40 kg and of 10 mg in children weighing ≥40 kg resulted in similar exposures and vWF:Ag suppression across age and weight groups. Despite the lack of pediatric clinical data, the results of this modeling and simulation analysis constituted the basis for the European extension of indication for caplacizumab (10 mg) to adolescents aged >12 years and with a body weight ≥40 kg. This represents a rare case in which regulatory authorities have deemed a modeling and simulation study robust enough to approve a variation of indication.

Pharmacokinetic modeling and simulation support for age- and weight-adjusted dosing of dabigatran etexilate in children with venous thromboembolism.

BACKGROUND: Dabigatran etexilate (DE), a direct oral thrombin inhibitor, has been evaluated in children with venous thromboembolism (VTE) using oral solution, pellets, or capsules. OBJECTIVES: This study evaluated DE pharmacokinetics (PK) in children with VTE and the appropriateness of a DE pediatric age- and weight-based dosing algorithm. PATIENTS/METHODS: A population PK model was fitted to data from four single-arm and one randomized, comparative pediatric VTE studies (358 children aged birth to <18 years; 2748 PK observations) and one healthy-adult study (32 males aged <40 years; 1523 PK observations) using nonlinear mixed-effects modeling. A stepwise, covariate, model-building procedure evaluated the influence of covariates (e.g., age, body weight, body surface area [BSA]-normalized renal function, and sex). The final model was used to evaluate the pediatric dosing algorithm, with simulations comparing pediatric trough exposure with reference exposure defined for the pediatric studies. RESULTS: The population PK of dabigatran was adequately described by a two-compartment model with first-order elimination and absorption. Age, weight, BSA-normalized renal function, and sex were statistically significant covariates (all P < .05). Apparent clearance increased with age (independently of body weight), diminished with decreasing BSA-normalized renal function, and was lower in females than males. All disposition parameters increased with body weight escalation (allometric scaling). Simulations confirmed that for all DE formulations, the final pediatric dosing algorithms achieved reference exposure without dose adjustment. CONCLUSIONS: Using a population PK model of DE for children with VTE, simulations showed that the final dosing algorithms were appropriate for all DE formulations; no dose titration was needed.

Model-Based Prediction of Plasma Concentration and Enterohepatic Circulation of Total Bile Acids in Humans.

Bile acids released postprandially can modify the rate and extent of lipophilic compounds' absorption. This study aimed to predict the enterohepatic circulation (EHC) of total bile acids (TBAs) in response to caloric intake from their spillover in plasma. A model for TBA EHC was combined with a previously developed gastric emptying (GE) model. Longitudinal gallbladder volumes and TBA plasma concentration data from 30 subjects studied after ingestion of four different test drinks were supplemented with literature data. Postprandial gallbladder refilling periods were implemented to improve model predictions. The TBA hepatic extraction was reduced with the high-fat drink. Basal and nutrient-induced gallbladder emptying rates were altered by type 2 diabetes (T2D). The model was predictive of the central trend and the variability of gallbladder volume and TBA plasma concentration for all test drinks. Integration of this model within physiological pharmacokinetic modeling frameworks could improve the predictions for lipophilic compounds' absorption considerably.

Population pharmacokinetic and pharmacodynamic properties of artesunate in patients with artemisinin sensitive and resistant infections in Southern Myanmar.

BACKGROUND: Artemisinins are the most effective anti-malarial drugs for uncomplicated and severe Plasmodium falciparum malaria. However, widespread artemisinin resistance in the Greater Mekong Region of Southeast Asia is threatening the possibility to control and eliminate malaria. This work aimed to evaluate the pharmacokinetic and pharmacodynamic properties of artesunate and its active metabolite, dihydroartemisinin, in patients with sensitive and resistant falciparum infections in Southern Myanmar. In addition, a simple nomogram previously developed to identify artemisinin resistant malaria infections was evaluated. METHODS: Fifty-three (n = 53) patients were recruited and received daily oral artesunate monotherapy (4 mg/kg) for 7 days. Frequent artesunate and dihydroartemisinin plasma concentration measurements and parasite microscopy counts were obtained and evaluated using nonlinear mixed-effects modelling. RESULTS: The absorption of artesunate was best characterized by a transit-compartment (n = 3) model, followed by one-compartment disposition models for artesunate and dihydroartemisinin. The drug-dependent parasite killing effect of dihydroartemisinin was described using an Emax function, with a mixture model discriminating between artemisinin sensitive and resistant parasites. Overall, 56% of the studied population was predicted to have resistant malaria infections. Application of the proposed nomogram to identify artemisinin-resistant malaria infections demonstrated an overall sensitivity of 90% compared to 55% with the traditional day-3 positivity test. CONCLUSION: The pharmacokinetic-pharmacodynamic properties of artesunate and dihydroartemisinin were well-characterized with a mixture model to differentiate between drug sensitive and resistant infections in these patients. More than half of all patients recruited in this study had artemisinin-resistant infections. The relatively high sensitivity of the proposed nomogram highlights its potential clinical usefulness.

An automated sampling importance resampling procedure for estimating parameter uncertainty.

Quantifying the uncertainty around endpoints used for decision-making in drug development is essential. In nonlinear mixed-effects models (NLMEM) analysis, this uncertainty is derived from the uncertainty around model parameters. Different methods to assess parameter uncertainty exist, but scrutiny towards their adequacy is low. In a previous publication, sampling importance resampling (SIR) was proposed as a fast and assumption-light method for the estimation of parameter uncertainty. A non-iterative implementation of SIR proved adequate for a set of simple NLMEM, but the choice of SIR settings remained an issue. This issue was alleviated in the present work through the development of an automated, iterative SIR procedure. The new procedure was tested on 25 real data examples covering a wide range of pharmacokinetic and pharmacodynamic NLMEM featuring continuous and categorical endpoints, with up to 39 estimated parameters and varying data richness. SIR led to appropriate results after 3 iterations on average. SIR was also compared with the covariance matrix, bootstrap and stochastic simulations and estimations (SSE). SIR was about 10 times faster than the bootstrap. SIR led to relative standard errors similar to the covariance matrix and SSE. SIR parameter 95% confidence intervals also displayed similar asymmetry to SSE. In conclusion, the automated SIR procedure was successfully applied over a large variety of cases, and its user-friendly implementation in the PsN program enables an efficient estimation of parameter uncertainty in NLMEM.

Population Pharmacokinetic and Pharmacodynamic Modeling of Artemisinin Resistance in Southeast Asia.

Orally administered artemisinin-based combination therapy is the first-line treatment against uncomplicated P. falciparum malaria worldwide. However, the increasing prevalence of artemisinin resistance is threatening efforts to treat and eliminate malaria in Southeast Asia. This study aimed to characterize the exposure-response relationship of artesunate in patients with artemisinin sensitive and resistant malaria infections. Patients were recruited in Pailin, Cambodia (n = 39), and Wang Pha, Thailand (n = 40), and received either 2 mg/kg/day of artesunate mono-therapy for 7 consecutive days or 4 mg/kg/day of artesunate monotherapy for 3 consecutive days followed by mefloquine 15 and 10 mg/kg for 2 consecutive days. Plasma concentrations of artesunate and its active metabolite, dihydroartemisinin, and microscopy-based parasite densities were measured and evaluated using nonlinear mixed-effects modeling. All treatments were well tolerated with minor and transient adverse reactions. Patients in Cambodia had substantially slower parasite clearance compared to patients in Thailand. The pharmacokinetic properties of artesunate and dihydroartemisinin were well described by transit-compartment absorption followed by one-compartment disposition models. Parasite density was a significant covariate, and higher parasite densities were associated with increased absorption. Dihydroartemisinin-dependent parasite killing was described by a delayed sigmoidal Emax model, and a mixture function was implemented to differentiate between sensitive and resistant infections. This predicted that 84% and 16% of infections in Cambodia and Thailand, respectively, were artemisinin resistant. The final model was used to develop a simple diagnostic nomogram to identify patients with artemisinin-resistant infections. The nomogram showed > 80% specificity and sensitivity, and outperformed the current practice of day 3 positivity testing.

Prediction of Improved Antimalarial Chemoprevention with Weekly Dosing of Dihydroartemisinin-Piperaquine.

Intermittent preventive treatment (IPT) is used to reduce malaria morbidity and mortality, especially in vulnerable groups such as children and pregnant women. IPT with the fixed dose combination of piperaquine (PQ) and dihydroartemisinin (DHA) is being evaluated as a potential mass treatment to control and eliminate artemisinin-resistant falciparum malaria. This study explored alternative DHA-PQ adult dosing regimens compared to the monthly adult dosing regimen currently being studied in clinical trials. A time-to-event model describing the concentration-effect relationship of preventive DHA-PQ administration was used to explore the potential clinical efficacy of once-weekly adult dosing regimens. Loading dose strategies were evaluated and the advantage of weekly dosing regimen was tested against different degrees of adherence. Assuming perfect adherence, three tablets weekly dosing regimen scenarios maintained malaria incidence of 0.2 to 0.3% per year compared to 2.1 to 2.6% for all monthly dosing regimen scenarios and 52% for the placebo. The three tablets weekly dosing regimen was also more forgiving (i.e., less sensitive to poor adherence), resulting in a predicted ∼4% malaria incidence per year compared to ∼8% for dosing regimen of two tablets weekly and ∼10% for monthly regimens (assuming 60% adherence and 35% interindividual variability). These results suggest that weekly dosing of DHA-PQ for malaria chemoprevention would improve treatment outcomes compared to monthly administration by lowering the incidence of malaria infections, reducing safety concerns about high PQ peak plasma concentrations and being more forgiving. In addition, weekly dosing is expected to reduce the selection pressure for PQ resistance.

In Vitro and In Vivo Modeling of Hydroxypropyl Methylcellulose (HPMC) Matrix Tablet Erosion Under Fasting and Postprandial Status.

PURPOSE: To develop a model linking in vitro and in vivo erosion of extended release tablets under fasting and postprandial status. METHODS: A nonlinear mixed-effects model was developed from the in vitro erosion profiles of four hydroxypropyl methylcellulose (HPMC) matrix tablets studied under a range of experimental conditions. The model was used to predict in vivo erosion of the HPMC matrix tablets in different locations of the gastrointestinal tract, determined by magnetic marker monitoring. In each gastrointestinal segment the pH was set to physiological values and mechanical stress was estimated in USP2 apparatus rotation speed equivalent. RESULTS: Erosion was best described by a Michaelis-Menten type model. The maximal HPMC release rate (VMAX) was affected by pH, mechanical stress, HPMC and calcium hydrogen phosphate content. The amount of HPMC left at which the release rate is half of VMAX depended on pH and calcium hydrogen phosphate. Mechanical stress was estimated for stomach (39.5 rpm), proximal (93.3 rpm) and distal (31.1 rpm) small intestine and colon (9.99 rpm). CONCLUSIONS: The in silico model accurately predicted the erosion profiles of HPMC matrix tablets under fasting and postprandial status and can be used to facilitate future development of extended release tablets.

Population pharmacokinetics of ticagrelor and AR-C124910XX in patients with prior myocardial infarction
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OBJECTIVE: The population pharmacokinetics of ticagrelor and its active metabolite AR-C124910XX were characterized following ticagrelor 60 mg or 90 mg twice daily oral long-term treatment in 4,426 patients with a history of myocardial infarction. METHODS: The ticagrelor and AR-C124910XX plasma concentration-time data were described by one-compartment models with first-order absorption or metabolite formation and elimination. RESULTS: Systemic exposure to ticagrelor and AR-C124910XX were stable over time. Ticagrelor apparent clearance (CL/F) was 17 L/h for the 60-mg and 15.4 L/h for the 90-mg dose. The CL/F of AR-C124910XX was 11.1 L/h for the 60-mg and 9.95 L/h for the 90-mg dose. Both ticagrelor and AR-C124910XX CL/F were independently influenced by body weight, sex, age, smoking, and Japanese ethnicity. Female sex and age > 75 years were the only categorical covariates, having more than 20% effect on AR-C124910XX CL/F. Ticagrelor CL/F was 6% higher and 11% lower, whereas AR-C124910XX CL/F was 26% higher and 34% lower for patients weighing 110 and 50 kg, respectively, compared with an 83 kg patient. CONCLUSIONS: The small differences in exposure to both ticagrelor and AR-C124910XX between demographic subgroups were in accordance with the consistent efficacy and safety outcomes observed across the population. The results were similar to those observed previously in patients with acute coronary syndromes.
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Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling.

Taking parameter uncertainty into account is key to make drug development decisions such as testing whether trial endpoints meet defined criteria. Currently used methods for assessing parameter uncertainty in NLMEM have limitations, and there is a lack of diagnostics for when these limitations occur. In this work, a method based on sampling importance resampling (SIR) is proposed, which has the advantage of being free of distributional assumptions and does not require repeated parameter estimation. To perform SIR, a high number of parameter vectors are simulated from a given proposal uncertainty distribution. Their likelihood given the true uncertainty is then approximated by the ratio between the likelihood of the data given each vector and the likelihood of each vector given the proposal distribution, called the importance ratio. Non-parametric uncertainty distributions are obtained by resampling parameter vectors according to probabilities proportional to their importance ratios. Two simulation examples and three real data examples were used to define how SIR should be performed with NLMEM and to investigate the performance of the method. The simulation examples showed that SIR was able to recover the true parameter uncertainty. The real data examples showed that parameter 95 % confidence intervals (CI) obtained with SIR, the covariance matrix, bootstrap and log-likelihood profiling were generally in agreement when 95 % CI were symmetric. For parameters showing asymmetric 95 % CI, SIR 95 % CI provided a close agreement with log-likelihood profiling but often differed from bootstrap 95 % CI which had been shown to be suboptimal for the chosen examples. This work also provides guidance towards the SIR workflow, i.e.,which proposal distribution to choose and how many parameter vectors to sample when performing SIR, using diagnostics developed for this purpose. SIR is a promising approach for assessing parameter uncertainty as it is applicable in many situations where other methods for assessing parameter uncertainty fail, such as in the presence of small datasets, highly nonlinear models or meta-analysis.

A strategy for residual error modeling incorporating scedasticity of variance and distribution shape.

Nonlinear mixed effects models parameters are commonly estimated using maximum likelihood. The properties of these estimators depend on the assumption that residual errors are independent and normally distributed with mean zero and correctly defined variance. Violations of this assumption can cause bias in parameter estimates, invalidate the likelihood ratio test and preclude simulation of real-life like data. The choice of error model is mostly done on a case-by-case basis from a limited set of commonly used models. In this work, two strategies are proposed to extend and unify residual error modeling: a dynamic transform-both-sides approach combined with a power error model (dTBS) capable of handling skewed and/or heteroscedastic residuals, and a t-distributed residual error model allowing for symmetric heavy tails. Ten published pharmacokinetic and pharmacodynamic models as well as stochastic simulation and estimation were used to evaluate the two approaches. dTBS always led to significant improvements in objective function value, with most examples displaying some degree of right-skewness and variances proportional to predictions raised to powers between 0 and 1. The t-distribution led to significant improvement for 5 out of 10 models with degrees of freedom between 3 and 9. Six models were most improved by the t-distribution while four models benefited more from dTBS. Changes in other model parameter estimates were observed. In conclusion, the use of dTBS and/or t-distribution models provides a flexible and easy-to-use framework capable of characterizing all commonly encountered residual error distributions.

Meta-analysis of Magnetic Marker Monitoring Data to Characterize the Movement of Single Unit Dosage Forms Though the Gastrointestinal Tract Under Fed and Fasting Conditions.

PURPOSE: To develop a model predicting movement of non-disintegrating single unit dosage forms (or "tablet") through the gastrointestinal tract and characterizing the effect of food intake, based on Magnetic Marker Monitoring data, allowing real-time location of a magnetically labeled formulation. METHODS: Five studies including 30 individuals in 94 occasions under 3 food status were considered. The mean residence time (MRT) of the tablet and the effect of food intake in proximal (PS) and distal stomach (DS), small intestine (SI), ascending (AC), transverse (TC) and descending colon (DC) were estimated using a Markov model for probabilities of movement. RESULTS: Under fasting conditions, tablet MRTs were 9.4 min in PS, 10.4 in DS, 246 in SI, 545 in AC, 135 in TC, and 286 in DC. A meal taken simultaneous to tablet intake prolonged tablet MRT to 99 min in PS and to 232 in DS; probability of gastric emptying increased of 89% each hour from 2.25 h after meal. The effect of a gastroileac reflex, caused by a secondary meal, accelerated the transit from terminal SI to AC. CONCLUSION: This model-based knowledge can be used as a part of mechanism-based models for drug absorption, applied for bottom-up predictions and/or top-down estimation.

Characterization of an in vivo concentration-effect relationship for piperaquine in malaria chemoprevention.

A randomized, placebo-controlled trial conducted on the northwest border of Thailand compared malaria chemoprevention with monthly or bimonthly standard 3-day treatment regimens of dihydroartemisinin-piperaquine. Healthy adult male subjects (N = 1000) were followed weekly during 9 months of treatment. Using nonlinear mixed-effects modeling, the concentration-effect relationship for the malaria-preventive effect of piperaquine was best characterized with a sigmoidal Emax relationship, where plasma concentrations of 6.7 ng/ml [relative standard error (RSE), 23%] and 20 ng/ml were found to reduce the hazard of acquiring a malaria infection by 50% [that is, median inhibitory concentration (IC50)] and 95% (IC95), respectively. Simulations of monthly dosing, based on the final model and published pharmacokinetic data, suggested that the incidence of malaria infections over 1 year could be reduced by 70% with a recently suggested dosing regimen compared to the current manufacturer's recommendations for small children (8 to 12 kg). This model provides a rational framework for piperaquine dose optimization in different patient groups.

PBPK models for the prediction of in vivo performance of oral dosage forms.

Drug absorption from the gastrointestinal (GI) tract is a highly complex process dependent upon numerous factors including the physicochemical properties of the drug, characteristics of the formulation and interplay with the underlying physiological properties of the GI tract. The ability to accurately predict oral drug absorption during drug product development is becoming more relevant given the current challenges facing the pharmaceutical industry. Physiologically-based pharmacokinetic (PBPK) modeling provides an approach that enables the plasma concentration-time profiles to be predicted from preclinical in vitro and in vivo data and can thus provide a valuable resource to support decisions at various stages of the drug development process. Whilst there have been quite a few successes with PBPK models identifying key issues in the development of new drugs in vivo, there are still many aspects that need to be addressed in order to maximize the utility of the PBPK models to predict drug absorption, including improving our understanding of conditions in the lower small intestine and colon, taking the influence of disease on GI physiology into account and further exploring the reasons behind population variability. Importantly, there is also a need to create more appropriate in vitro models for testing dosage form performance and to streamline data input from these into the PBPK models. As part of the Oral Biopharmaceutical Tools (OrBiTo) project, this review provides a summary of the current status of PBPK models available. The current challenges in PBPK set-ups for oral drug absorption including the composition of GI luminal contents, transit and hydrodynamics, permeability and intestinal wall metabolism are discussed in detail. Further, the challenges regarding the appropriate integration of results from in vitro models, such as consideration of appropriate integration/estimation of solubility and the complexity of the in vitro release and precipitation data, are also highlighted as important steps to advancing the application of PBPK models in drug development. It is expected that the "innovative" integration of in vitro data from more appropriate in vitro models and the enhancement of the GI physiology component of PBPK models, arising from the OrBiTo project, will lead to a significant enhancement in the ability of PBPK models to successfully predict oral drug absorption and advance their role in preclinical and clinical development, as well as for regulatory applications.

External validation of the bilirubin-atazanavir nomogram for assessment of atazanavir plasma exposure in HIV-1-infected patients.

Atazanavir increases plasma bilirubin levels in a concentration-dependent manner. Due to less costly and readily available assays, bilirubin has been proposed as a marker of atazanavir exposure. In this work, a previously developed nomogram for detection of suboptimal atazanavir exposure is validated against external patient populations. The bilirubin nomogram was validated against 311 matching bilirubin and atazanavir samples from 166 HIV-1-infected Norwegian, French, and Italian patients on a ritonavir-boosted regimen. In addition, the nomogram was evaluated in 56 Italian patients on an unboosted regimen. The predictive properties of the nomogram were validated against observed atazanavir plasma concentrations. The use of the nomogram to detect non-adherence was also investigated by simulation. The bilirubin nomogram predicted suboptimal exposure in the patient populations on a ritonavir-boosted regimen with a negative predictive value of 97% (95% CI 95-100). The bilirubin nomogram and monitoring of atazanavir concentrations had similar predictive properties for detecting non-adherence based on simulations. Although both methods performed adequately during a period of non-adherence, they had lower predictive power to detect past non-adherence episodes. Using the bilirubin nomogram for detection of suboptimal atazanavir exposure in patients on a ritonavir-boosted regimen is a rapid and cost-effective alternative to routine measurements of the actual atazanavir exposure in plasma. Its application may be useful in clinical settings if atazanavir concentrations are not available.

Population pharmacokinetic and pharmacodynamic modeling of amodiaquine and desethylamodiaquine in women with Plasmodium vivax malaria during and after pregnancy.

Amodiaquine is effective for the treatment of Plasmodium vivax malaria, but there is little information on the pharmacokinetic and pharmacodynamic properties of amodiaquine in pregnant women with malaria. This study evaluated the population pharmacokinetic and pharmacodynamic properties of amodiaquine and its biologically active metabolite, desethylamodiaquine, in pregnant women with P. vivax infection and again after delivery. Twenty-seven pregnant women infected with P. vivax malaria on the Thai-Myanmar border were treated with amodiaquine monotherapy (10 mg/kg/day) once daily for 3 days. Nineteen women, with and without P. vivax infections, returned to receive the same amodiaquine dose postpartum. Nonlinear mixed-effects modeling was used to evaluate the population pharmacokinetic and pharmacodynamic properties of amodiaquine and desethylamodiaquine. Amodiaquine plasma concentrations were described accurately by lagged first-order absorption with a two-compartment disposition model followed by a three-compartment disposition of desethylamodiaquine under the assumption of complete in vivo conversion. Body weight was implemented as an allometric function on all clearance and volume parameters. Amodiaquine clearance decreased linearly with age, and absorption lag time was reduced in pregnant patients. Recurrent malaria infections in pregnant women were modeled with a time-to-event model consisting of a constant-hazard function with an inhibitory effect of desethylamodiaquine. Amodiaquine treatment reduced the risk of recurrent infections from 22.2% to 7.4% at day 35. In conclusion, pregnancy did not have a clinically relevant impact on the pharmacokinetic properties of amodiaquine or desethylamodiaquine. No dose adjustments are required in pregnancy.

Serial correlation in optimal design for nonlinear mixed effects models.

In population modeling two sources of variability are commonly included; inter individual variability and residual variability. Rich sampling optimal design (more samples than model parameters) using these models will often result in a sampling schedule where some measurements are taken at exactly the same time point, thereby maximizing the signal-to-noise ratio. This behavior is a result of not appropriately taking into account error generation mechanisms and is often clinically unappealing and may be avoided by including intrinsic variability, i.e. serially correlated residual errors. In this paper we extend previous work that investigated optimal designs of population models including serial correlation using stochastic differential equations to optimal design with the more robust, and analytic, AR(1) autocorrelation model. Further, we investigate the importance of correlation strength, design criteria and robust designs. Finally, we explore the optimal design properties when estimating parameters with and without serial correlation. In the investigated examples the designs and estimation performance differs significantly when handling serial correlation.

Rapid sample size calculations for a defined likelihood ratio test-based power in mixed-effects models.

Efficient power calculation methods have previously been suggested for Wald test-based inference in mixed-effects models but the only available alternative for Likelihood ratio test-based hypothesis testing has been to perform computer-intensive multiple simulations and re-estimations. The proposed Monte Carlo Mapped Power (MCMP) method is based on the use of the difference in individual objective function values (ΔiOFV) derived from a large dataset simulated from a full model and subsequently re-estimated with the full and reduced models. The ΔiOFV is sampled and summed (∑ΔiOFVs) for each study at each sample size of interest to study, and the percentage of ∑ΔiOFVs greater than the significance criterion is taken as the power. The power versus sample size relationship established via the MCMP method was compared to traditional assessment of model-based power for six different pharmacokinetic and pharmacodynamic models and designs. In each case, 1,000 simulated datasets were analysed with the full and reduced models. There was concordance in power between the traditional and MCMP methods such that for 90% power, the difference in required sample size was in most investigated cases less than 10%. The MCMP method was able to provide relevant power information for a representative pharmacometric model at less than 1% of the run-time of an SSE. The suggested MCMP method provides a fast and accurate prediction of the power and sample size relationship.