Modelling the progression of illicit substance use patterns from real-world evidence.

AIMS: To investigate an innovative pharmacometrics approach that addresses the challenges of using real-world evidence to model the progression of illicit substance use. METHODS: The modelling strategy analysed real-world data from the National Longitudinal Study of Adolescent to Adult Health (AddHealth) survey using survival analyses and differential equations. Respondents were categorized into drug-naïve, active users and nonusers. The transitions between categories were modelled using interval-censored parametric survival analysis. The resulting hazard rate functions were used as time-dependent rate constants in a differential equation system. Covariate models for sex and depression status were assessed. RESULTS: AddHealth enrolled 6504 American teenagers (median age 16 years, range 11-21 years); this cohort was followed with five interviews over a 22-year period; the median age at the last interview was 38 years (range 34-45 years). The percentages of illicit drug users at Interviews 1-5 were 7.7%, 5.9%, 15.8%, 21.4% and 0.98%, respectively. The generalized gamma distribution emerged as the preferred model for the survival functions for transitions between categories. Age-dependent prevalence was obtained from the differential equation system. Active drug use was more prevalent in males, increased in adolescence and college years, peaked at 24 years, and decreased to low levels by 35 years. Depression, which was more frequent in females, increased the drug-naïve-active user transition rates but not the active user-nonuser and nonuser-active user transition rates. The evidence did not support an interaction between sex and depression. CONCLUSIONS: The model provided a satisfactory approximation for the age-dependent progression of illicit substance use from preadolescence to early middle age.

Unraveling the Influence of Age, IQ, Education, and Negative Symptoms on Neurocognitive Performance in Schizophrenia: A Conditional Inference Tree Analysis.

INTRODUCTION: The complex nature of neurocognitive impairment in schizophrenia has been discussed in light of the mixed effects of antipsychotic drugs, psychotic symptoms, dopamine D2 receptor blockade, and intelligence quotient (IQ). These factors have not been thoroughly examined before. METHODS: This study conducted a comprehensive re-analysis of the CATIE data using machine learning techniques, in particular Conditional Inference Tree (CTREE) analysis, to investigate associations between neurocognitive functions and moderating factors such as estimated trough dopamine D2 receptor blockade with risperidone, olanzapine, or ziprasidone, Positive and Negative Syndrome Scale (PANSS), and baseline IQ in 573 patients with schizophrenia. RESULTS: The study reveals that IQ, age, and education consistently emerge as significant predictors across all neurocognitive domains. Furthermore, higher severity of PANSS-negative symptoms was associated with lower cognitive performance scores in several domains. CTREE analysis, in combination with a genetic algorithm approach, has been identified as particularly insightful for illustrating complex interactions between variables. Lower neurocognitive function was associated with factors such as age>52 years, IQ<94/95,<12/13 education years, and more pronounced negative symptoms (score<26). CONCLUSIONS: These findings emphasize the multifaceted nature of neurocognitive functioning in patients with schizophrenia, with the PANSS-negative score being an important predictor. This gives rise to a role in addressing negative symptoms as a therapeutic objective for enhancing cognitive impairments in these patients. Further research must examine nonlinear relationships among various moderating factors identified in this work, especially the role of D2 occupancy.

Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM.

To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of "temperature" hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction.

Authors' response to letter to editor.

Authors' Response to Letter to Editor from Hinpetch Daungsupawong and Viroj Wiwanitkit.

pyDarwin machine learning algorithms application and comparison in nonlinear mixed-effect model selection and optimization.

Forward addition/backward elimination (FABE) has been the standard for population pharmacokinetic model selection (PPK) since NONMEM® was introduced. We investigated five machine learning (ML) algorithms (Genetic algorithm [GA], Gaussian process [GP], random forest [RF], gradient boosted random tree [GBRT], and particle swarm optimization [PSO]) as alternatives to FABE. These algorithms were applied to PPK model selection with a focus on comparing the efficiency and robustness of each of them. All machine learning algorithms included the combination of ML algorithms with a local downhill search. The local downhill search consisted of systematically changing one or two "features" at a time (a one-bit or a two-bit local search), alternating with the ML methods. An exhaustive search (all possible combinations of model features, N = 1,572,864 models) was the gold standard for robustness, and the number of models examined leading prior to identification of the final model was the metric for efficiency.All algorithms identified the optimal model when combined with the two-bit local downhill search. GA, RF, GBRT, and GP identified the optimal model with only a one-bit local search. PSO required the two-bit local downhill search. In our analysis, GP was the most efficient algorithm as measured by the number of models examined prior to finding the optimal (495 models), and PSO exhibited the least efficiency, requiring 1710 unique models before finding the best solution. Additionally, GP was also the algorithm that needed the longest elapsed time of 2975.6 min, in comparison with GA, which only required 321.8 min.

Systemic exposure to hydroxychloroquine and its relationship with outcome in severely ill COVID-19 patients in New York City.

AIM: To investigate the relationship between systemic exposure to hydroxychloroquine (HCQ) and its metabolite desethylhydroxychloroquine (DHCQ) and clinical outcome in severely ill patients treated with a standard oral dose regimen of HCQ during the first wave of COVID-19 in New York City. METHODS: We correlated retrospective clinical data with drug exposure prospectively assessed from convenience samples using population pharmacokinetics and Bayesian estimation. Systemic exposure was assessed in 215 patients admitted to ICU or COVID-ward for whom an interleukin-6 level was requested and who were still alive 24 hours after the last dose of HCQ. Patients received oral HCQ 600 mg twice daily on day 1 followed by 4 days of 400 mg daily. RESULTS: Fifty-three precent of the patients were intubated at 5.4 ± 6.4 days after admission and 26.5% died at an average of 32.2 ± 19.1 days. QTc at admission was 448 ± 34 ms. Systemic exposure to HCQ and DHCQ demonstrated substantial variability. Cumulative area under the serum concentration-time curve up to infinity for HCQ was 71.4 ± 19.3 h mg/L and for DHCQ 56.5 ± 28.3 h mg/L. Variability in systemic exposure was not clearly explained by renal function, liver function or inflammatory state. In turn, systemic exposure did not correlate with intubation status, survival or QTc prolongation. CONCLUSION: This study in severely ill patients was not able to find any relationship between systemic exposure to HCQ and DHCQ and clinical outcome at a routine dose regimen and adds to the growing body of evidence that oral HCQ does not alter the course of disease in COVID-19 patients.

Integration of DNA sequencing with population pharmacokinetics to improve the prediction of irinotecan exposure in cancer patients.

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.

A Physiologically-Based Pharmacokinetic Model of the Brain Considering Regional Lipid Variance.

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 mechanistic understanding. A physiologically based pharmacokinetic model presented here incorporates multiple means of drug distribution to assemble a model for understanding potential factors that may determine the distribution of drugs across various regions of the brain, including both intra- and extracellular regions. Two classes of parameters are presented. The first concerns regional gross anatomic variability of the brain; the second concerns estimation of unbound fractions of drugs using know membrane phospholipid heterogeneity derived from regional lipid content. The model was then tested by comparing its outcomes to data from published human pharmacokinetic studies of acetaminophen, morphine, and phenytoin. The alignment of model predictions in the plasma, CSF, and tissue concentrations with the published data from studies of those three drugs suggests that the model can be a template for identifying drug localization in the brain. Clearly, knowledge of differentiated drug distribution in the brain is a requisite step in postulating pharmacodynamic and certain disease mechanisms. SIGNIFICANCE STATEMENT: This study concerns the application of heterogenous lipid distribution in brain tissue to predict regional variations in drug distribution in the brain via a mathematical model, thus expanding upon the current understanding of mechanisms of drug distribution in the central nervous system.

Clozapine Once-Daily Versus Divided Dosing Regimen: A Cross-sectional Study in Japan.

OBJECTIVE: Clozapine is generally recommended to be prescribed in a divided dosing regimen based on its relatively short plasma half-life. However, there has been little evidence to support the superiority of divided dosing of clozapine over once-daily dosing. To our knowledge, there have been no studies examining differences in actual plasma concentrations or adverse effects between the 2 dosing strategies of clozapine. We aimed to compare actual plasma concentrations of clozapine between once-daily and divided dosing regimens, and to examine the relationships of these regimens with psychiatric symptoms and adverse effects of clozapine. METHODS: We analyzed data from 108 participants of a previous study conducted in 2 hospitals in Japan. A population pharmacokinetic model was used to estimate the peak and trough plasma concentrations of clozapine based on actual plasma concentrations. We evaluated psychiatric symptoms with the Brief Evaluation of Psychosis Symptom Domains and adverse effects of clozapine with the Glasgow Antipsychotic Side-effects Scale for Clozapine. RESULTS: The estimated peak and trough plasma concentrations of clozapine did not differ significantly between once-daily and divided dosing regimens. There were no significant differences in psychiatric symptoms except for depression/anxiety or subjective adverse effects of clozapine between the 2 dosing strategies. CONCLUSIONS: Our findings tentatively support the feasibility and clinical utility of once-daily dosing of clozapine in clinical practice. Further studies are needed to replicate these findings and determine causality between dosing strategies and clinical outcomes.

Machine learning in pharmacometrics: Opportunities and challenges.

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.

MAP Bayesian modelling combining striatal dopamine receptor occupancy and plasma concentrations to optimize antipsychotic dose regimens in individual patients.

AIMS: Develop a robust and user-friendly software tool for the prediction of dopamine D2 receptor occupancy (RO) in patients with schizophrenia treated with either olanzapine or risperidone, in order to facilitate clinician exploration of the impact of treatment strategies on RO using sparse plasma concentration measurements. METHODS: Previously developed population pharmacokinetic models for olanzapine and risperidone were combined with a pharmacodynamic model for D2 RO and implemented in the R programming language. Maximum a posteriori Bayesian estimation was used to provide predictions of plasma concentration and RO based on sparse concentration sampling. These predictions were then compared to observed plasma concentration and RO. RESULTS: The average (standard deviation) response times of the tools, defined as the time required for the application to predict parameter values and display the output, 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 (95% confidence interval) of predicted vs. observed concentrations were 3.73 ng/mL (-2.42-9.87) and 10.816 ng/mL (6.71-14.93) for olanzapine, and 0.46 ng/mL (-4.56-5.47) and 6.68 ng/mL (3.57-9.78) for risperidone and its active metabolite (9-OH risperidone). Mean error and root mean squared error 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: Our monitoring software predicts concentration-time profiles and the corresponding D2 RO from sparsely sampled concentration measurements in an accessible and accurate form.

A population pharmacokinetic model based on HPTN 077 of long-acting injectable cabotegravir for HIV PrEP.

AIMS: 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. The aim of this study is 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 analysed CAB-LA PK data using nonlinear mixed-effects modelling 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-occasion 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.

Development of a genetic algorithm and NONMEM workbench for automating and improving population pharmacokinetic/pharmacodynamic model selection.

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.

Towards safer risperidone prescribing in Alzheimer's disease.

BACKGROUND: In 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. AIMS: This 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). METHOD: A 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. RESULTS: The 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. CONCLUSIONS: Our 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.

Does sodium oxybate inhibit brain dopamine release in humans? An exploratory neuroimaging study.

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.

Population pharmacokinetic modeling of intramuscular and oral dexamethasone and betamethasone in Indian women.

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 [Formula: see text]. 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 ([Formula: see text] of 9.29 L/h, steady-state volume ([Formula: see text] of 56.4 L, IM absorption constant [Formula: see text] of 0.460 1/h and oral absorption constant ([Formula: see text] of 0.936 1/h. Betamethasone parameters were CL/FIM of 5.95 L/h, [Formula: see text] of 72.4 L, [Formula: see text] of 0.971 1/h, and [Formula: see text] 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.

Population pharmacodynamic modeling of intramuscular and oral dexamethasone and betamethasone effects on six biomarkers with circadian complexities in Indian women.

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.

Multicenter phase 2 trial of nintedanib in advanced nonpancreatic neuroendocrine tumors.

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.

A multicentre phase 1b/2 study of tivozanib in patients with advanced inoperable hepatocellular carcinoma.

BACKGROUND: Hepatocellular 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. METHODS: We 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. RESULTS: Twenty-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. CONCLUSIONS: Although 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 REGISTRATION: ClinicalTrials.gov NCT01835223, registered on 15 April 2013.

Model-Guided Antipsychotic Dose Reduction in Schizophrenia: A Pilot, Single-Blind Randomized Controlled Trial.

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.