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Modeling and simulation of the central nervous system provides a tool for understanding and predicting the distribution of small molecules throughout the brain tissue and cerebral spinal fluid (CSF), and these efforts often rely on empirical data to make predictions of distributions to move toward a better

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Purpose: The best phase II design and endpoint for growth inhibitory agents is controversial. We simulated phase II trials by resampling patients from a positive (sorafenib vs. placebo; TARGET) and a negative (AE941 vs. placebo) phase III trial in metastatic renal cancer to compare the ability of various designs and endpoints to predict the known results.Experimental Design: A total of 770 and 259 patients from TARGET and the AE 941 trial, respectively, were resampled (5,000 replicates) to simulate phase II trials with α = 0.10 (one-sided). Designs/endpoints: single arm, two-stage with response rate (RR) by Response Evaluation Criteria in Solid Tumors (RECIST; 37 patients); and randomized, two arm (20–35 patients per arm) with RR by RECIST, mean log ratio of tumor sizes (log ratio), progression-free survival (PFS) rate at 90 days (PFS-90), and overall PFS.Results: Single-arm trials were positive with RR by RECIST in 55% and 1% of replications for sorafenib and AE 941, respectively. Randomized trials versus placebo with 20 patients per arm were positive with RR by RECIST in 55% and 7%, log ratio in 88% and 25%, PFS-90 in 64% and 15%, and overall PFS in 69% and 9% of replications for sorafenib and AE 941, respectively.Conclusions: Compared with the single-arm design and the randomized design comparing PFS, the randomized phase II design with the log ratio endpoint has greater power to predict the positive phase III result of sorafenib in renal cancer, but a higher false positive rate for the negative phase III result of AE 941. Clin Cancer Res; 18(8); 2309–15. ©2012 AACR.

CCR Translation for This Article from Resampling Phase III Data to Assess Phase II Trial Designs and Endpoints

BACKGROUND: Cabotegravir delivered as a long-acting intramuscular injection has shown superior efficacy to oral tenofovir-emtricitabine as pre-exposure prophylaxis (PrEP) for HIV. Cabotegravir pharmacokinetics (PK), like those of other long-acting depot preparations, exhibit variability between individuals and between injection occasions. AIM: To describe the population pharmacokinetics of long-acting cabotegravir (CAB-LA). METHODS: Using available PK measurements from 133 participants in the HIV Prevention Trials Network (HPTN) 077 trial, we analyzed CAB-LA PK data using nonlinear mixed-effects modeling to develop a population PK model. RESULTS: A two-compartment model with first order absorption best described the CAB-LA PK. The analysis identified between-occasional variability (BOV, i.e., differences in PK within one individual from one injection to the next) as a significant covariate affecting the absorption rate, with an estimated contribution of BOV to PK variability on the absorption rate (Ka) of 38.5%. Sex and body weight were identified as significant covariates influencing the absorption rate and apparent clearance of CAB-LA after intramuscular injection at various doses and frequencies. Male participants had 67% higher Ka than female participants. Serially adding to the model body weight on clearance, Sex on Ka, and BOV on Ka led to a decrease in the objective function value (OFV) of 24.4, 36, and 321.4, respectively. CONCLUSION: The public availability of this model will facilitate and enable a wide variety of future clinically relevant simulations to inform the optimal use of CAB-LA.

Population pharmacokinetic/pharmacodynamic (PK/PD) analysis was performed for extensive data for differing dosage forms and routes for dexamethasone (DEX) and betamethasone (BET) in 48 healthy nonpregnant Indian women in a partial and complex cross-over design. Single doses of 6 mg dexamethasone phosphate (DEX-P), betamethasone phosphate (BET-P), or 1:1 mixture of betamethasone phosphate and acetate (BET-PA) were administered orally (PO) or intramuscularly (IM) where each woman enrolled in a two-period cross-over study. Plasma concentrations collected over 96 h were described with a two-compartment model with differing PO and IM first-order absorption inputs. Overall, BET exhibited slower clearance, similar volume of distribution, faster absorption, and longer persistence than DEX with BET acetate producing extremely slow absorption but full bioavailability of BET. Six biomarkers were assessed over a 24-h baseline period with four showing circadian rhythms with complex baselines. These baselines and the strong responses seen after drug dosing were fitted with various indirect response models using the Laplace estimation methods in NONMEM 7.4. Both the PK and six biomarker responses were well-described with modest variability likely due to the homogeneous ages, weights, and ethnicities of the women. The drugs either inhibited or stimulated the influx processes with some models requiring joint inclusion of drug effects on circadian cortisol suppression. The biomarkers and order of sensitivity (lowest IC50/SC50 to highest) were: cortisol, T-helper cells, basophils, glucose, neutrophils, and T-cytotoxic cells. DEX sensitivities were generally greater than BET with corresponding mean ratios for these biomarkers of 2.86, 1.27, 1.72, 1.27, 2.69, and 1.06. Overall, the longer PK (e.g. half-life) of BET, but lesser PD activity (e.g. higher IC50), produces single-dose response profiles that appear quite similar, except for the extended effects from BET-PA. This comprehensive population modeling effort provides the first detailed comparison of the PK profiles and six biomarker responses of five commonly used dosage forms of DEX and BET in healthy women.

The explosive growth in medical devices, imaging and diagnostics, computing, and communication and information technologies in drug development and healthcare has created an ever-expanding data landscape that the pharmacometrics (PMX) research community must now traverse. The tools of machine learning (ML) have emerged as a powerful computational approach in other data-rich disciplines but its effective utilization in the pharmaceutical sciences and PMX modelling is in its infancy. ML-based methods can complement PMX modelling by enabling the information in diverse sources of big data, e.g. population-based public databases and disease-specific clinical registries, to be harnessed because they are capable of efficiently identifying salient variables associated with outcomes and delineating their interdependencies. ML algorithms are computationally efficient, have strong predictive capabilities and can enable learning in the big data setting. ML algorithms can be viewed as providing a computational bridge from big data to complement PMX modelling. This review provides an overview of the strengths and weaknesses of ML approaches vis-à-vis population methods, assesses current research into ML applications in the pharmaceutical sciences and provides perspective for potential opportunities and strategies for the successful integration and utilization of ML in PMX.

Population analysis of pharmacokinetic data for five differing dosage forms and routes for dexamethasone and betamethasone in 48 healthy nonpregnant Indian women was performed that accounted for a partial and complex cross-over design. Single doses of 6 mg dexamethasone phosphate (DEX-P), betamethasone phosphate (BET-P), or 1:1 mixture of betamethasone phosphate and acetate (BET-PA) were administered orally (PO) or intramuscularly (IM). Plasma concentrations collected for two periods over 96 h were described with a two-compartment model with differing PO and IM first-order absorption inputs. Clearances and volumes were divided by the IM bioavailability $${F}_{IM}$$ F IM . The homogeneous ages, body weights, and ethnicity of the women obviated covariate analysis. Parameter estimates were obtained by the Laplace estimation method implemented in NONMEM 7.4. Typical values for dexamethasone were clearance ($${CL/F}_{IM})$$ C L / F IM ) of 9.29 L/h, steady-state volume ($${{V}_{ss}/F}_{IM})$$ V ss / F IM ) of 56.4 L, IM absorption constant $$\left({k}_{aIM}\right)$$ k aIM of 0.460 1/h and oral absorption constant ($${k}_{aPO})$$ k aPO ) of 0.936 1/h. Betamethasone parameters were CL/FIM of 5.95 L/h, $${Vss/F}_{IM}$$ V s s / F IM of 72.4 L, $${k}_{aIM}$$ k aIM of 0.971 1/h, and $${k}_{aPO}$$ k aPO of 1.21 1/h. The PO to IM F values were close to 1.0 for both drugs. The terminal half-lives averaged about 7.5 h for DEX, 17 h for BET, and 78 h for BET from BET-PA with the latter reflecting very slow release of BET from the acetate ester. Overall, BET exhibited slower clearance, larger volume of distribution, faster absorption, and longer persistence than DEX. These data may be useful in considering exposures when substituting one form of corticosteroid for another.

Background: Irinotecan (CPT-11) is an anticancer agent widely used to treat adult solid tumours. Large interindividual variability in the clearance of irinotecan and SN-38, its active and toxic metabolite, results in highly unpredictable toxicity. Methods: In 217 cancer patients treated with intravenous irinotecan single agent or in combination, germline DNA was used to interrogate the variation in 84 genes by next-generation sequencing. A stepwise analytical framework including a population pharmacokinetic model with SNP- and gene-based testing was used to identify demographic/clinical/genetic factors that influence the clearance of irinotecan and SN-38. Results: Irinotecan clearance was influenced by rs4149057 in SLCO1B1, body surface area, and co-administration of 5-fluorouracil/leucovorin/bevacizumab. SN-38 clearance was influenced by rs887829 in UGT1A1, pre-treatment total bilirubin, and EGFR rare variant burden. Within each UGT1A1 genotype group, elevated pre-treatment total bilirubin and/or presence of at least one rare variant in EGFR resulted in significantly lower SN-38 clearance. The model reduced the interindividual variability in irinotecan clearance from 38 to 34% and SN-38 clearance from 49 to 32%. Conclusions: This new model significantly reduced the interindividual variability in the clearance of irinotecan and SN-38. New genetic factors of variability in clearance have been identified.

Background Antiangiogenic-targeting agents have low response rates in patients with nonpancreatic neuroendocrine tumors (NETs). Nintedanib is an oral antiangiogenic agent that has inhibitory effects on the fibroblast growth factor receptor, which is highly expressed in NETs. The authors hypothesized that nintedanib would be active in patients with nonpancreatic NETs. Methods Patients with advanced, grade 1 or 2, nonpancreatic NETs who were receiving a stable dose of somatostatin analogue were enrolled. Nintedanib was administered at a dose of 200 mg twice daily in 28-day cycles. The primary endpoint was progression-free survival (PFS) at 16 weeks. Results Thirty-two patients were enrolled, and 30 were evaluable for the primary outcome. Most had radiographic disease progression within 12 months before enrollment. The 16-week PFS rate was 83%, and the median PFS and overall survival were 11.0 months and 32.7 months, respectively. Nintedanib was well tolerated and delayed deterioration in quality of life. The baseline serotonin level had a strong, positive correlation with activated but exhausted T cells. Conclusions Nintedanib is active in nonpancreatic NETs. The immunosuppressive effect of serotonin should be targeted in future clinical trials.

BackgroundHepatocellular carcinoma (HCC) is a major cause of cancer-related death. It is a highly vascular tumour with multiple angiogenic factors, most importantly vascular endothelial growth factor (VEGF), involved in HCC progression. Tivozanib is an oral inhibitor of VEGFR-1/2/3 with promising activity against HCC in vivo.MethodsWe conducted a phase 1b/2 study of tivozanib in patients with advanced HCC. The safety, dosing, pharmacokinetics, pharmacodynamics, and preliminary antineoplastic efficacy of tivozanib were evaluated.ResultsTwenty-seven patients received at least one dose of tivozanib. Using a 3+3 design, the recommended phase 2 dose (RP2D) of tivozanib was determined to be 1 mg per os once daily, 21 days on–7 days off. The median progression-free and overall survival were 24 weeks and 9 months, respectively, for patients treated at RP2D. The overall response rate was 21%. Treatment was well tolerated. A significant decrease in soluble plasma VEGFR-2 was noted, assuring adequate target engagement.ConclusionsAlthough this study did not proceed to stage 2, there was an early efficacy signal with a very favourable toxicity profile. A phase 1/2 trial of tivozanib in combination with durvalumab is currently underway.Trial registrationClinicalTrials.gov NCT01835223, registered on 15 April 2013.

Applying data mining and machine learning (ML) techniques to clinical data might identify predictive biomarkers for diabetic nephropathy (DN), a common complication of type 2 diabetes mellitus (T2DM). A retrospective analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial was intended to identify such factors using ML. The longitudinal data were stratified by time after patient enrollment to differentiate early and late predictors. Our results showed that Random Forest and Simple Logistic Regression methods exhibited the best performance among the evaluated algorithms. Baseline values for glomerular filtration rate (GFR), urinary creatinine, urinary albumin, potassium, cholesterol, low-density lipoprotein, and urinary albumin to creatinine ratio were identified as DN predictors. Early predictors were the baseline values of GFR, systolic blood pressure, as well as fasting plasma glucose (FPG) and potassium at month 4. Changes per year in GFR, FPG, and triglycerides were recognized as predictors of late development. In conclusion, ML-based methods successfully identified predictive factors for DN among patients with T2DM.

Purpose/background Patients with schizophrenia as well as their psychiatrists are hesitant to reduce the antipsychotic dose in fear of relapse. To overcome such dilemmas, we developed models to individually calculate an oral dose that corresponds to a given target dopamine D2 receptor occupancy. Methods/procedures In this pilot, 52-week single-blind randomized controlled trial, 35 clinically stable patients with schizophrenia receiving either risperidone or olanzapine monotherapy were randomly assigned to dose reduction (n = 17) or dose maintenance group (n = 18). In the former group, baseline doses were reduced to the doses corresponding to 65% D2 occupancy (the lower end of therapeutic window) at trough that were calculated from randomly collected plasma concentrations using our models. Findings/results In the dose reduction group, doses of risperidone and olanzapine were decreased from 4.2 ± 1.9 to 1.4 ± 0.4 and 12.8 ± 3.9 to 6.7 ± 1.8 mg/d, whereas the doses in the dose maintenance group were 4.3 ± 1.9 and 15.8 ± 4.6 mg/d, respectively. Twelve subjects (70.5%) and 13 subjects (72.2%) in the dose reduction and dose maintenance groups completed the study (P = 0.604), whereas 3 subjects (18.8%) and none dropped out because of clinical worsening in the dose reduction and dose maintenance groups, respectively. There were not significant differences in score changes in Positive and Negative Syndrome Scale between the 2 groups but in Positive subscale scores in the Clinical Global Impression-Schizophrenia (0.4 ± 0.7 in the dose reduction group vs -0.1 ± 0.7 in the dose maintenance group, P = 0.029). Implications/conclusions Although our model-guided dose reduction strategy was found to be comparable with no-dose change in terms of dropout rates, safety issues have to be further examined.

Bridging fundamental approaches to model optimization for pharmacometricians, systems pharmacologists and statisticians is a critical issue. These fields rely primarily on Maximum Likelihood and Extended Least Squares metrics with iterative estimation of parameters. Our research combines adaptive chaos synchronization and grid search to estimate physiological and pharmacological systems with emergent properties by exploring deterministic methods that are more appropriate and have potentially superior performance than classical numerical approaches, which minimize the sum of squares or maximize the likelihood. We illustrate these issues with an established model of cortisol in human with nonlinear dynamics. The model describes cortisol kinetics over time, including its chaotic oscillations, by a delay differential equation. We demonstrate that chaos synchronization helps to avoid the tendency of the gradient-based optimization algorithms to end up in a local minimum. The subsequent analysis illustrates that the hybrid adaptive chaos synchronization for estimation of linear parameters with coarse-to-fine grid search for optimal values of non-linear parameters can be applied iteratively to accurately estimate parameters and effectively track trajectories for a wide class of noisy chaotic systems. Electronic supplementary material The online version of this article (10.1007/s10928-019-09629-4) contains supplementary material, which is available to authorized users.

Professor Panos Macheras is a pioneering scientist in pharmacokinetics, pharmacodynamics and biopharmaceutics. His many important contributions to pharmaceutical science are reviewed.

Aim A robust and user-friendly software tool was developed for the prediction of dopamine D2 receptor occupancy (RO) in patients with schizophrenia treated with either olanzapine or risperidone. This tool can facilitate clinician exploration of the impact of treatment strategies on RO using sparse plasma concentration measurements. Methods Previously developed population pharmacokinetic (PPK) models for olanzapine and risperidone were combined with a PD model for D2 receptor occupancy (RO) and implemented in the R programming language. MAP Bayesian estimation was used to provide predictions of plasma concentration and receptor occupancy and based on sparse PK measurements. Results The average (standard deviation) response times of the tools were 2.8 (3.1) and 5.3 (4.3) seconds for olanzapine and risperidone, respectively. The mean error (95% confidence interval) and root mean squared error (RMSE, 95% CI) of predicted versus observed concentrations were 3.73 ng/mL (-2.42 – 9.87) and 10.816 (6.71 – 14.93) for olanzapine, and 0.46 ng/mL (-4.56 – 5.47) and 6.68 (3.57 – 9.78) for risperidone and its active metabolite (9-OH risperidone). Mean error and RMSE of RO were -1.47% (-4.65 – 1.69) and 5.80 (3.89 – 7.72) for olanzapine and -0.91% (-7.68 – 5.85) and 8.87 (4.56 – 13.17) for risperidone. Conclusion Treatment of schizophrenia with antipsychotics offers unique challenges and requires careful monitoring to establish the optimal dosing regimen. Our monitoring software predicts RO in a reliable and accurate form.

Objective To establish in an exploratory neuroimaging study whether γ-hydroxybutyrate (sodium oxybate [SO]), a sedative, anti-narcoleptic drug with abuse potential, transiently inhibits striatal dopamine release in the human. Methods Ten healthy participants (30 years; 6M, 4F) and one participant with narcolepsy received a baseline positron emission tomography scan of [C-11]raclopride, a D2/3 dopamine receptor radioligand sensitive to dopamine occupancy, followed approximately one week later by an oral sedative 3g dose of SO and two [C-11]raclopride scans (1 h, 7 h post SO). Plasma SO levels and drowsiness duration were assessed. Results No significant changes were detected in [C-11]raclopride binding in striatum overall 1 or 7 h after SO, but a small non-significant increase in [C-11]raclopride binding, implying decreased dopamine occupancy, was noted in limbic striatal subdivision at one hour (+6.5%; p uncorrected = 0.045; +13.2%, narcolepsy participant), returning to baseline at 7 h. A positive correlation was observed between drowsiness duration and percent change in [C-11]raclopride binding in limbic striatum (r = 0.73; p = 0.017). Conclusions We did not find evidence in this sample of human subjects of a robust striatal dopamine change, as was reported in non-human primates. Our preliminary data, requiring extension, suggest that a 3g sedative SO dose might cause slight transient inhibition of dopamine release in limbic striatum.

Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a machine learning (ML) approach to improve phase II or proof‐of‐concept trials designed to address unmet medical needs in treating schizophrenia. Diagnostic data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial were used to develop a binary classification ML model predicting individual patient response as either “improvement,” defined as greater than 20% reduction in total Positive and Negative Syndrome Scale (PANSS) score, or “no improvement,” defined as an inadequate treatment response (

BackgroundIn the treatment of psychosis, agitation and aggression in Alzheimer's disease, guidelines emphasise the need to ‘use the lowest possible dose’ of antipsychotic drugs, but provide no information on optimal dosing.AimsThis analysis investigated the pharmacokinetic profiles of risperidone and 9-hydroxy (OH)-risperidone, and how these related to treatment-emergent extrapyramidal side-effects (EPS), using data from The Clinical Antipsychotic Trials of Intervention Effectiveness in Alzheimer's Disease study (Clinicaltrials.gov identifier: NCT00015548).MethodA statistical model, which described the concentration–time course of risperidone and 9-OH-risperidone, was used to predict peak, trough and average concentrations of risperidone, 9-OH-risperidone and ‘active moiety’ (combined concentrations) (n = 108 participants). Logistic regression was used to investigate the associations of pharmacokinetic biomarkers with EPS. Model-based predictions were used to simulate the dose adjustments needed to avoid EPS.ResultsThe model showed an age-related reduction in risperidone clearance (P < 0.0001), reduced renal elimination of 9-OH-risperidone (elimination half-life 27 h), and slower active moiety clearance in 22% of patients, (concentration-to-dose ratio: 20.2 (s.d. = 7.2) v. 7.6 (s.d. = 4.9) ng/mL per mg/day, Mann–Whitney U-test, P < 0.0001). Higher trough 9-OH-risperidone and active moiety concentrations (P < 0.0001) and lower Mini-Mental State Examination (MMSE) scores (P < 0.0001), were associated with EPS. Model-based predictions suggest the optimum dose ranged from 0.25 mg/day (85 years, MMSE of 5), to 1 mg/day (75 years, MMSE of 15), with alternate day dosing required for those with slower drug clearance.ConclusionsOur findings argue for age- and MMSE-related dose adjustments and suggest that a single measure of the concentration-to-dose ratio could be used to identify those with slower drug clearance.

Background Epirubicin is metabolized by uridine glucuronosyltransferase 2B7 (UGT2B7). Patients homozygous for the minor allele (CC) in the UGT2B7 -161 promoter polymorphism have lower clearance and significantly higher rates of leukopenia compared to wild-type homozygote (TT) or heterozygote (CT) patients. This study was designed to determine if TT and CT genotype patients could tolerate a higher epirubicin dose compared to CC genotype patients. Patients and Methods We studied women with histologically confirmed non-metastatic, invasive breast cancer who were scheduled to receive at least three cycles of FE100C in the (neo)adjuvant setting. Patients received standard-dose FE100C during the first 21-day cycle. Based on genotype, the epirubicin dose was escalated in the second and third cycles to 115 and 130 mg/m2 or to 120 and 140 mg/m2 for CT and TT genotype patients, respectively. The main outcome measurements were myelosuppression and dose-limiting toxicity. These were analyzed for relationships with the three genotypes. Results Forty-five patients were enrolled (10 CC, 21 CT, and 14 TT genotypes) and received 100 mg/m2 of epirubicin in the first cycle. Twelve and 10 TT patients were dose escalated at the second and third cycles, respectively; 16 CT patients were dose escalated at the second and third cycles. Leukopenia, but not febrile neutropenia, was genotype and dose dependent and increased in patients with CT and TT genotypes as their dose was increased. However, the third-cycle leukopenia rates were comparable to patients with the CC genotype receiving standard-dose epirubicin. Conclusion Pharmacogenetically guided epirubicin dosing is well tolerated and allowed dose escalation without increased toxicity.

The current approach to selection of a population PK/PD model is inherently flawed as it fails to account for interactions between structural, covariate, and statistical parameters. Further, the current approach requires significant manual and redundant model modifications that heavily lend themselves to automation. Within the discipline of numerical optimization it falls into the "local search" category. Genetic algorithms are a class of algorithms inspired by the mathematics of evolution. GAs are general, powerful, robust algorithms and can be used to find global optimal solutions for difficult problems even in the presence of non-differentiable functions, as is the case in the discrete nature of including/excluding model components in search of the best performing mixed-effects PK/PD model. A genetic algorithm implemented in an R-based NONMEM workbench for identification of near optimal models is presented. In addition to the GA capabilities, the workbench supports modeling efforts by: (1) Organizing and displaying models in tabular format, allowing the user to sort, filter, edit, create, and delete models seamlessly, (2) displaying run results, parameter estimates and precisions, (3) integrating xpose4 and PsN to facilitate generation of model diagnostic plots and run PsN scripts, (4) running regression models between post-hoc parameter estimates and covariates. This approach will further facilitate the scientist to shift efforts to focus on model evaluation, hypotheses generation, and interpretation and applications of resulting models.

BackgroundIn the treatment of psychosis, agitation and aggression in Alzheimer’s disease (AD), guidelines emphasise the need to ‘use the lowest possible dose’ of antipsychotic drugs, but provide no information on optimal dosing.AimsThis analysis investigated the pharmacokinetic profiles of risperidone and active metabolite, 9-hydroxy (OH)-risperidone, and how this related to emergent extrapyramidal side effects (EPS), using data from The Clinical Antipsychotic Trials of Intervention Effectiveness-AD study.MethodA statistical model, which described the concentration-time course of risperidone and 9-OH-risperidone, was used to predict peak, trough and average concentrations of risperidone, 9-OH-risperidone and ‘active moiety’ (combined concentrations) (108 CATIE-AD participants). Logistic regression was used to investigate the associations of pharmacokinetic biomarkers with EPS. Model based predictions were used to simulate the dose adjustments needed to avoid EPS.ResultsThe model showed an age-related reduction in risperidone clearance (pppConclusionsOur findings argue for age- and MMSE -related dose adjustments and suggest that a single plasma sample could be used to identify those with slower drug clearance.

Irinotecan (CPT-11) is an anticancer agent widely used in the treatment of a variety of adult solid tumors. The objective of this study was to develop an optimal sampling strategy model that accurately estimates pharmacokinetic parameters of CPT-11 and its active metabolite, SN-38. This study included 221 patients with advanced solid tumors or lymphoma receiving CPT-11 single or combination therapy with 5-fluorouracil (5-FU)/leucovorin (LV) (FOLFIRI) plus bevacizumab from 4 separate clinical trials. Population pharmacokinetic analysis of CPT-11 and SN-38 was performed by non-linear mixed effects modeling. The optimal sampling strategy model was developed using D-optimality with expected distribution approach. The pharmacokinetic profiles of CPT-11 and SN-38 were best described by a 3- and 2-compartment model, respectively, with first-order elimination. Body surface area and co-administration with 5-FU/LV plus bevacizumab were significant covariates (p < 0.01) for volumes of the central compartment of CPT-11 and SN-38, and clearance of CPT-11. Pre-treatment total bilirubin and co-administration with 5-FU/LV and bevacizumab were significant covariates (p < 0.01) for clearance of SN-38. Accurate and precise predictive performance (r2 > 0.99, -2 < bias (%ME) < 0, precision (% RMSE) < 12) of both CPT-11 and SN-38 was achieved using: (i) 6 fixed sampling times collected at 1.5, 3.5, 4, 5.75, 22, 23.5 hours post-infusion; or (ii) 1 fixed time and 2 sampling windows collected at 1.5, [3-5.75], [22-23.5] hours post-infusion. The present study demonstrates that an optimal sampling design with three blood samples achieves accurate and precise pharmacokinetic parameter estimates for both CPT-11 and SN-38.

Pre-exposure prophylaxis (PrEP) has emerged as a promising strategy for preventing the transmission of HIV. Although only one formulation is currently approved for PrEP, research into both new compounds and new delivery systems for PrEP regimens offer intriguing challenges from the perspective of pharmacokinetic and pharmacodynamic modeling. This review aims to provide an overview the current modeling landscape for HIV PrEP, focused on PK/PD and QSP models relating to antiretroviral agents. Both current PrEP treatments and new compounds that show promise as PrEP agents are highlighted, as well as models of uncommon administration routes, predictions based on models of mechanism of action and viral dynamics, and issues related to adherence to therapy. The spread of human immunodeficiency virus (HIV) remains one of the foremost global health concerns. In the absence of a vaccine, other prophylactic strategies have been developed to prevent HIV transmission. One approach, known as pre-exposure prophylaxis (PrEP), allows HIV-negative individuals who are at high risk of exposure to the virus, be it through an HIV-positive sexual partner or through the shared use of drug injection equipment, to substantially reduce the risk of developing an HIV infection. PrEP is a relatively recent approach to combating the HIV epidemic, with the only currently approved treatment being Truvada, a daily oral antiretroviral (ARV) therapy initially indicated in the treatment of active HIV-1 infections, but approved for HIV PrEP in 2012. Although PrEP therapy has consistently demonstrated high efficacy in preventing HIV infection, this efficacy is dependent on patient adherence to the prescribed treatment regimen. This can present a significant problem in low- and middle-income countries, which may lack the infrastructure to provide sufficient access to PrEP medication to maintain daily dosing regimens. Furthermore, while the conventional approach has generally been to advocate for continuous administration akin to regimens used for viral suppression in infected patients, there has been some discussion of whether a better treatment paradigm might be to push for PrEP therapy primarily during those known periods of heightened exposure risk, while relying on post-exposure prophylaxis regimens to prevent infection after unanticipated exposures during low-risk periods. These considerations have led to a push for the development of long-duration and on-demand PrEP formulations, including subdermal and subcutaneous implants, slow-release intramuscular depot injections, vaginal and rectal antimicrobial gels, and intravaginal rings and dissolving films. PrEP therapy is a quickly evolving field, with a variety of antiretroviral compounds and formulations under investigation. This review aims to report on notable drugs and formulations from a pharmacokinetic/pharmacodynamic (PK/PD) modeling perspective. Given the nature of PrEP as a preventive therapy designed for long-term use, clinical trials for PrEP therapies can last for months or even years, particularly in the case of long-duration formulations. Furthermore, in contrast to antiretroviral trials in infected patients, pharmacodynamic endpoints in PrEP therapies are difficult to quantify, as the primary endpoint for efficacy is generally the rate of seroconversion. Computational modeling approaches offer flexible and powerful tools to provide insight into drug behavior in clinical settings, and can ultimately reduce the time, expense, and patient burden incurred in the development of PrEP therapies.

Aims Treatment of prolactinomas with ergoline dopamine agonists can be complicated by intolerance and resistance. This study investigated the pharmacokinetics and pharmacodynamics of the nonergot dopamine agonist ropinirole, to assess its therapeutic potential as a novel therapy for prolactinomas. Methods Five female subjects with prolactinomas participated in this dose-response study. Subjects received up to three doses of ropinirole (0.5, 1.0 and 2.0 mg), each on separate occasions. Frequent blood samples for prolactin and ropinirole were collected for 24 h following drug administration. Data were analysed using noncompartmental and compartmental pharmacokinetic-pharmacodynamic (PKPD) techniques. Results Seven 24-h curves revealed increased systemic drug exposure with increasing ropinirole doses. Ropinirole concentrations peaked at 4.4 ± 2.7 h and exhibited a half-life of 5.8 ± 1.7 h. A dose-dependent prolactin nadir occurred 4.4 ± 1.2 h after drug intake and prolactin concentrations transiently normalized in two of five subjects. PKPD modelling revealed that single-dose PK of ropinirole is dose-independent and can be described with a one-compartment model with linear absorption and elimination. An indirect response model successfully captures the inhibitory effect of ropinirole on prolactin secretion and incorporates time-dependent receptor desensitization for three of five subjects whose prolactin concentrations nadired before ropinirole reached Cmax . Conclusions This data-rich study has informed our understanding of the clinical pharmacokinetics and pharmacodynamics of ropinirole, which are successfully captured by the proposed semi-mechanistic PKPD model. This model can be used to further investigate the PKPD of ropinirole and may facilitate the identification of optimal dose regimens for the treatment of prolactinomas and the establishment of a new therapeutic option for patients impacted by this rare disease.

Aims A physiologically-based pharmacokinetic (PBPK) model of the vaginal space was developed with the aim of predicting concentrations in the vaginal and cervical space. These predictions can be used to optimize the probability of success of vaginally administered dapivirine (DPV) for HIV prevention. We focus on vaginal delivery using either a ring or film. Methods A PBPK model describing the physiological structure of the vaginal tissue and fluid was defined mathematically and implemented in MATLAB. Literature reviews provided estimates for relevant physiological and physiochemical parameters. Drug concentration-time profiles were simulated in luminal fluids, vaginal tissue and plasma after administration of ring or film. Patient data were extracted from published clinical trials and used to test model predictions. Results The DPV ring simulations tested the two dosing regimens and predicted PK profiles and area under the curve of luminal fluids (29 079 and 33 067 mg h l-1 in groups A and B, respectively) and plasma (0.177 and 0.211 mg h l-1 ) closely matched those reported (within one standard deviation). While the DPV film study reported drug concentration at only one time point per patient, our simulated profiles pass through reported concentration range. Conclusions HIV is a major public health issue and vaginal microbicides have the potential to provide a crucial, female-controlled option for protection. The PBPK model successfully simulated realistic representations of drug PK. It provides a reliable, inexpensive and accessible platform where potential effectiveness of new compounds and the robustness of treatment modalities for pre-exposure prophylaxis can be evaluated.

The objective of this study was to investigate possible circadian pattern of psychotic symptoms in patients with schizophrenia, which could be reflected on the dosing schedule/regimen, i.e. chrono-pharmacology. Patients with schizophrenia (ICD-10) who reported having auditory hallucination, receiving monotherapy with risperidone, olanzapine or paliperidone for at least two weeks were included. The subjects were provided a diary and asked to record the time and duration of auditory hallucinations during the eight time periods (i.e. 00:00–03:00, 03:00–06:00, 06:00–09:00, 09:00–12:00, 12:00–15:00, 15:00–18:00, 18:00–21:00, and 21:00–24:00). In the diary, times of medication doses and sleep were also recorded. Time and degree of peak and trough dopamine D2 receptor blockade with antipsychotics were estimated from 2 sparsely collected plasma drug concentrations. The prevalence and duration of auditory hallucinations were statistically examined among the eight time periods, respectively. Forty-nine patients participated in this study (mean ± SD age, 50.7 ± 14.8 years; 36 men (73.5%); 34 inpatients (69.4%)). Auditory hallucinations occurred most frequently and lasted for the longest duration in the period of 18:00–21:00 (75.5% (37/49) and 1.37 ± 1.67 h). This happened despite the fact that the difference in D2 receptor occupancy between the peak and trough was less than 2%, indicating a stable drug delivery. Since the dopamine D2 receptor blockade by antipsychotics was stable, the nocturnal circadian pattern found in this study may reflect intrinsic dopaminergic fluctuation or generally quieter environments at night. These circadian patterns may be considered to devise individualized treatment approach in the context of “chrono-pharmacology” for patients with schizophrenia.

Tumor doubling time can significantly affect the outcome of anticancer therapy, but it is very challenging to determine. Here, we present a statistical approach that extracts doubling times from progression-free survival (PFS) plots, which inherently contains information regarding the growth of solid tumors. Twelve cancers were investigated and multiple PFS plots were evaluated for each type. The PFS plot showing fastest tumor growth was deemed to best represent the inherent growth kinetics of the solid tumor, and selected for further analysis. The exponential tumor growth rates were extracted from each PFS plot, along with associated variabilities, which ultimately allowed for the estimation of solid tumor doubling times. The mean simulated doubling times for pancreatic cancer, melanoma, hepatocellular carcinoma (HCC), renal cell carcinoma, triple negative breast cancer, non-small cell lung cancer, hormone receptor positive (HR+) breast cancer, human epidermal growth factor receptor-2 positive (HER-2+) breast cancer, gastric cancer, glioblastoma multiforme, colorectal cancer, and prostate cancer were 5.06, 3.78, 3.06, 2.67, 2.38, 2.40, 4.31, 4.12, and 3.84 months, respectively. For all cancers, clinically reported doubling times were within the estimated ranges. For all cancers, except HCC, the growth rates were best characterized by a log-normal distribution. For HCC, the gamma distribution best described the data. The statistical approach presented here provides a qualified method for extracting tumor growth rates and doubling times from PFS plots. It also allows estimation of the distributional characteristics for tumor growth rates and doubling times in a given patient population.

Background: There have been scarce data on the distribution of clozapine concentrations in comparison with the recommended range (350–600 ng/ml) or their relationship with side effects among patients with treatment-resistant schizophrenia. Furthermore, no studies have assessed the association between side effects and overall exposure to the drug by calculating the 24-h area-under-curve (AUC). Methods: In- and outpatients with schizophrenia or schizoaffective disorder (ICD-10) who were receiving a stable dose of clozapine for ⩾2 weeks were included. Side effects were assessed using the Glasgow antipsychotic side-effects scale for clozapine (GASS-C). Using two collected plasma samples, plasma clozapine and norclozapine concentrations at peak and trough and their 24-h AUC were estimated using population pharmacokinetic models. Results: A total of 108 patients completed the study (mean ± SD age, 43.0 ± 10.1 years; clozapine dose, 357.5 ± 136.9 mg/day); 33 patients (30.6%) showed estimated trough concentrations of clozapine within the recommended range (350–600 ng/ml) whereas the concentrations were higher and lower than this range among 37 (43.5%) and 28 (25.9%) patients (%), respectively. There were no significant correlations between estimated peak or trough concentrations or 24-h AUC of both clozapine or norclozapine, and GASS-C total or individual scores. No significant differences were found between GASS-C total or individual item scores between the patients with estimated trough concentrations of clozapine of >600 ng/ml and the other subjects. Conclusion: The results suggest that clozapine or norclozapine concentrations are not linked directly to the extent of side effects experienced in clozapine-treated patients with treatment-resistant schizophrenia while the cross-sectional study design limits the interpretation of any causal relationships. These findings indicate that side effects associated with clozapine may occur at any dose or concentration.

OBJECTIVE Clozapine has strong recommendations for therapeutic drug monitoring. While factors that influence interindividual variation in plasma clozapine levels have been extensively reported, intraindividual variation remains poorly studied. We employed a population pharmacokinetic approach to assess intraindividual variations in plasma levels of both clozapine and N-desmethylclozapine, as well as the impact of smoking on this variability. METHODS Patients who were initiated on clozapine from January 2009 to December 2010 and who provided at least 2 plasma samples were included in this study. The observed concentrations of clozapine and N-desmethylclozapine were applied in a Bayesian pharmacokinetic modeling approach by using a previously published pharmacokinetic model from an independent sample to compute a predicted concentration. The predicted concentrations of clozapine and N-desmethylclozapine were then compared with the observed concentrations in the form of a ratio: predicted-to-observed concentration ratio (Cpred/Cobs). The coefficient of variation of the Cpred/Cobs ratios was taken as a measure of intraindividual variation. RESULTS A total of 723 plasma levels from 61 patients were included in this analysis. The coefficient of variation of Cpred/Cobs ratios for clozapine and N-desmethylclozapine were 29.8% (SD = 17.2%) and 27.4% (SD = 16.4%), respectively. Though values were higher, smoking did not have a significant effect on coefficients of variation of clozapine (33.5% vs 26.3%, P = .184) or N-desmethylclozapine (30.7% vs 24.2%, P = .100). CONCLUSIONS Clinicians need to be aware of intraindividual variability and not assume that plasma levels are static. If plasma levels are used to guide dosing of clozapine, serial measurements rather than a single level might be necessary to make an informed clinical decision. The clinical implications of intraindividual variability in plasma clozapine levels need further study.

Objective We recently reported an unexpected interaction between olanzapine and sertraline in a population being treated for psychotic depression. Contrary to knowledge of cytochrome p450 interactions sertraline increased apparent clearance of olanzapine by 30%. Here we examined the pharmacokinetics of sertraline in the same population. Existing studies suggest that sertraline apparent clearance is significantly increased in male subjects and suggested an age/sex interaction. Methods We studied subjects undergoing combination of sertraline/olanzapine treatment for psychotic depression in the Study of the Pharmacotherapy of Psychotic Depression. Nonlinear mixed effect modelling software was used to examine the sertraline pharmacokinetics, evaluating age, sex, race, and olanzapine exposure as covariates. Results Eighty-seven subjects (median age 62 years, 28 male subjects, 11 African-Americans) provided 138 samples for sertraline concentration. Olanzapine exposure had a 14.8-fold range. A one compartment model with combined residual error described the sertraline concentration data adequately. Half-life and sex effect on sertraline apparent clearance (males averaging 50% higher (p

In mathematical pharmacology, models are constructed to confer a robust method for optimizing treatment. The predictive capability of pharmacological models depends heavily on the ability to track the system and to accurately determine parameters with reference to the sensitivity in projected outcomes. To closely track chaotic systems, one may choose to apply chaos synchronization. An advantageous byproduct of this methodology is the ability to quantify model parameters. In this paper, we illustrate the use of chaos synchronization combined with Nelder-Mead search to estimate parameters of the well-known Kirschner-Panetta model of IL-2 immunotherapy from noisy data. Chaos synchronization with Nelder-Mead search is shown to provide more accurate and reliable estimates than Nelder-Mead search based on an extended least squares (ELS) objective function. Our results underline the strength of this approach to parameter estimation and provide a broader framework of parameter identification for nonlinear models in pharmacology.

Introduction: Due to high inter-individual variability in peripheral pharmacokinetic parameters, dosing of antipsychotics currently relies on clinical trial-and-error, and predicting antipsychotic plasma concentrations before changing a dose has been a challenge. Methods: Patients with schizophrenia receiving a stable dose of olanzapine were included. 2 plasma samples were collected at 2 given time points for the measurement of plasma olanzapine concentrations. At least 7 days after a dosage change of olanzapine, a third sample was collected. The plasma concentration of the third sample was predicted in a blinded fashion using a mixed-effects model with NONMEM ® , using the following information: the 2 baseline plasma concentrations, the interval between the last dose and blood draw, and clinical and demographic information. Results: 31 subjects (mean±SD age=56.0±11.6; 19 men) were enrolled. The mean prediction (95% confidence interval) errors were 1.6 (−2.8 to 6.0) ng/mL. A highly significant correlation was observed between the observed and predicted concentrations of the third sample (r=0.91, p Discussion: Plasma olanzapine concentrations following an actual dosage change can be predicted in advance with a high degree of certainty.

Reliable, noninvasive methods for diagnosing and prognosing sinusoidal obstruction syndrome (SOS) early after hematopoietic cell transplantation (HCT) are needed. We used a quantitative mass spectrometry–based proteomics approach to identify candidate biomarkers of SOS by comparing plasma pooled from 20 patients with and 20 patients without SOS. Of 494 proteins quantified, we selected 6 proteins (L-Ficolin, vascular cell adhesion molecule-1 [VCAM1], tissue inhibitor of metalloproteinase-1, von Willebrand factor, intercellular adhesion molecule-1, and CD97) based on a differential heavy/light isotope ratio of at least 2 fold, information from the literature, and immunoassay availability. Next, we evaluated the diagnostic potential of these 6 proteins and 5 selected from the literature (suppression of tumorigenicity-2 [ST2], angiopoietin-2 (ANG2), hyaluronic acid [HA], thrombomodulin, and plasminogen activator inhibitor-1) in samples from 80 patients. The results demonstrate that together ST2, ANG2, L-Ficolin, HA, and VCAM1 compose a biomarker panel for diagnosis of SOS. L-Ficolin, HA, and VCAM1 also stratified patients at risk for SOS as early as the day of HCT. Prognostic Bayesian modeling for SOS onset based on L-Ficolin, HA, and VCAM1 levels on the day of HCT and clinical characteristics showed >80% correct prognosis of SOS onset. These biomarkers may provide opportunities for preemptive intervention to minimize SOS incidence and/or severity.

Clinical evidence and expert opinion support using a combination of an antipsychotic and an antidepressant when treating major depression with psychotic features. We characterized the impact of sertraline co-administration on olanzapine clearance in psychotic depression using population pharmacokinetic methods.The Study of Pharmacotherapy for Psychotic Depression (STOP-PD) randomized 259 participants to olanzapine plus placebo or olanzapine plus sertraline. Olanzapine was started at 2.5-5 mg/day and sertraline at 25-50 mg/day. Doses were increased to a maximum of 20 mg/day for olanzapine and 200 mg/day for sertraline. Up to four olanzapine concentration samples were collected during the 12-week trial and 12-week continuation phase. We used NONMEM (Version VII) for population pharmacokinetic analysis, assessing effects of the covariates sex, African American origin, smoking, age, and sertraline co-administration.Population pharmacokinetic analysis comprised 336 samples from 175 individuals. The structural model published by Bigos et al. was sufficient to describe the olanzapine data adequately: a one-compartment model with first-order absorption and elimination, using an additive residual error structure with the absorption rate constant fixed to 0.5. Sertraline co-administration significantly increased olanzapine apparent clearance (p 0.005) by 25-35 % depending on the patient characteristics included. Male sex was associated with a significantly increased clearance. Age and race did not have a significant impact on clearance.Contrary to expectations from the knowledge of cytochrome P450 interactions, sertraline increased olanzapine apparent clearance. Plausible explanations include patients treated with sertraline having poorer adherence to olanzapine, or the impact of sertraline inhibition of transporters resulting in increased intracellular concentrations and thus access to metabolizing enzymes.

Objective Blockade of dopamine D2 receptors with antipsychotics above 65% is associated with optimal chance of clinical response although recent data suggest a lower threshold for the maintenance treatment of schizophrenia. The objective of this study was to prospectively examine whether such continuous high blockade would be necessary for maintenance treatment. Method In this single-blind, 52-week, randomized controlled trial, clinically stable patients with schizophrenia receiving risperidone or olanzapine were randomly assigned to the continuous D2 blockade (i.e. an estimated trough D2 blockade of > 65%) or non-continuous blockade group (i.e. an estimated peak level of > 65% with an estimated trough level of Results Sixty-eight subjects (34 in each group) were enrolled. Twenty-six (76.5%) and thirty-one (91.2%) subjects completed the study in the continuous and non-continuous blockade groups, respectively, without any significant group difference. No significant differences were found on any of the assessment scales between the two groups. The degree of dosage change was small in both groups. Conclusion These results offer support that the threshold for D2 receptor blockade in the maintenance treatment can be lower than 65%. However, these preliminary findings have to be confirmed through double-blind, larger scale trials with longer follow-up periods.

Tranexamic acid (TXA), an effective anti-fibrinolytic agent that is cleared by glomerular filtration, is used widely for cardiopulmonary bypass (CPB) surgery. However, an effective dosing regimen has not been fully developed in patients with renal impairment. The aims of this study were to characterize the inter-patient variability associated with pharmacokinetic parameters and to recommend a new dosing adjustment based on the BART dosing regimen for CPB patients with chronic renal dysfunction (CRD). Recently published data on CPB patients with normal renal function (n = 15) were re-examined with a two-compartment model using the ADAPT5® and NONMEMVII® to identify covariates that explain inter-patient variability and to ascertain whether sampling strategies might affect parameter estimation. A series of simulations was performed to adjust the BART dosing regimen for CPB patients with renal impairment. Based on the two-compartmental model, the number of samples obtained after discontinuation of TXA infusion was found not to be critical in parameter estimation (p > 0.05). Both body weight and creatinine clearance were identified as significant covariates (p < 0.005). Simulations showed significantly higher than normal TXA concentrations in CRD patients who received the standard dosing regimen in the BART trial. Adjustment of the maintenance infusion rate based on the percent reduction in renal clearance resulted in predicted plasma TXA concentrations that were safe and therapeutic (~100 mg·L−1). Our proposed dosing regimen, with consideration of renal function, is predicted to maintain effective target plasma concentrations below those associated with toxicity for patients with renal failure for CPB. Copyright © 2015 John Wiley & Sons, Ltd.