Pharmacokinetic-pharmacodynamic modeling of severity levels of extrapyramidal side effects with markov elements

Exposure-response modeling for nausea incidence for cotadutide using a Markov modeling approach

Cotadutide is a dual glucagon-like peptide-1 (GLP-1)/glucagon receptor agonist. Gastrointestinal adverse effects are known to be associated with GLP-1 receptor agonism and can be mitigated through tolerance development via a gradual up-titration. This analysis aimed to characterize the relationship between exposure and nausea incidence and to optimize titration schemes. The model was developed with pooled data from cotadutide-administrated studies. Three different modeling approaches, proportional odds (PO), discrete-time Markov, and two-stage discrete-time Markov models, were employed to characterize the exposure-nausea relationship. The severity of nausea was modeled as different states (non-nausea, mild, and moderate/severe). The most appropriate model was selected to perform the covariate analysis, and the final covariate model was used to simulate the nausea event rates for various titration scenarios. The two Markov models demonstrated comparable performance and were better than the PO model. The covariate analysis was conducted with the standard Markov model for operational simplification and identified disease indications (NASH, obesity) and sex as covariates on Markov parameters. The simulations indicated that the biweekly titration with twofold dose escalation is superior to other titration schemes with a relatively low predicted nausea event rate at 600 μg (25%) and a shorter titration interval (8 weeks) to reach the therapeutic dose. The model can be utilized to optimize starting dose and titration schemes for other therapeutics in clinical trials to achieve an optimal risk-benefit balance and reach the therapeutic dose with minimal titration steps.

Subcutaneous Marzeptacog Alfa (Activated) for On-Demand Treatment of Bleeding Events in Subjects With Hemophilia A or B With Inhibitors

Marzeptacog alfa (MarzAA) is under development for subcutaneous treatment of episodic bleeds in patients with hemophilia A/B and was studied in a phase III trial evaluating MarzAA compared with standard-of-care (SoC) for on-demand use. The work presented here aimed to evaluate MarzAA and SoC treatment of bleeding events on a standardized four-point efficacy scale (poor, fair, good, and excellent). Two continuous-time Markov modeling approaches were explored; a four-state model analyzing all four categories of bleeding improvement and a two-state model analyzing a binarized outcome (treatment failure (poor/fair), and treatment success (good/excellent)). Different covariates impacting improvement of bleeding episodes as well as a putative relationship between MarzAA exposure and improvement of bleeding episodes were evaluated. In the final four-state model, higher baseline diastolic blood pressure and higher age (> 33 years of age) were found to negatively and positively impact improvement of bleeding condition, respectively. Bleeding events occurring in knees and ankles were found to improve faster than bleeding events at other locations. The covariate effects had most impact on early treatment success (≤ 3 hours) whereas at later timepoints (> 12 hours), treatment success was similar for all patients indicating that these covariates might be clinically relevant for early treatment response. A statistically significant relationship between MarzAA zero-order absorption and improvement of bleedings (P < 0.05) were identified albeit with low precision. No statistically significant difference in treatment response between MarzAA and intravenous SoC was identified, indicating the potential of MarzAA for treatment of episodic bleeding events with a favorable subcutaneous administration route.

Rituximab pharmacokinetic and pharmacokinetic-pharmacodynamic evaluation based on a study in diffuse large B-cell lymphoma: Influence of tumor size on pharmacokinetic and assessment of pharmacokinetic similarity

Dr. Reddy's Laboratories rituximab (DRL_RI; Dr. Reddy's Laboratories SA, Basel, Switzerland) is under development as a rituximab biosimilar. Study RI-01-002 (Clinical Trials Registry - India/2012/11/003129), comparing DRL_RI to the reference medicinal product (RMP) MabThera® (Roche, Grenzach-Wyhlen, Germany), demonstrated pharmacokinetic (PK) equivalence and showed comparable pharmacodynamic, efficacy, safety, and immunogenicity profiles. We used data from the same study to perform population PK and PK-pharmacodynamic analyses: first exploring possible factors influencing the PK similarity assessment between products and then performing simulations to investigate the impact of tumor size on rituximab PK. Nonlinear mixed-effects models for PK, tumor size, tumor size-PK, and tumor response were developed independently. The final PK model included drug product as a dose-scaling parameter and predicted a 6.75% higher dose reaching the system in RMP-treated patients. However, when tumor size was included in the tumor size-PK model, the drug product effect was no longer observed. The model rather indicated that patients with larger tumor size have higher clearance. Further simulations confirmed that higher baseline tumor size is associated to slightly lower rituximab exposure. Tumor response, described by a continuous-time Markov model, did not differ between drug products. Both had higher effects during the first 20 weeks of treatment. Also, the model described a subpopulation of nonresponders to treatment (42%) with faster transitions to a worse state. The different rituximab exposure initially detected between drug products (6.75%) was shown using PK/PK-pharmacodynamic analysis to be attributed to a tumor size imbalance between treatment groups. PK/PK-pharmacodynamic analyses may contribute to PK similarity assessments.

Bridging Fixed Dose to Body Weight-based Regimen of Adalimumab in Paediatric Ulcerative Colitis Using a Pharmacometric Modelling Approach: Case Study with the Phase 3 ENVISION I Trial

BACKGROUND AND AIMS

The Phase 3 study ENVISION I demonstrated efficacy and safety of adalimumab in paediatric patients with moderate to severe ulcerative colitis. The protocol-specified high-dose adalimumab regimen was numerically more efficacious than the standard-dose regimen. The objective of this work was to bridge a fixed-dosing regimen to the protocol-specified high-induction/high-maintenance, body weight-based dosing regimen studied in ENVISION I, using a pharmacometrics modelling and simulation approach.

METHODS

A stepwise strategy was implemented, including developing an adalimumab paediatric population pharmacokinetic model; using this model to determine a fixed-dosing regimen in paediatric ulcerative colitis patients which achieves similar concentrations to those observed in ENVISION I patients; determining adalimumab exposure-response relationship using population pharmacokinetic/pharmacodynamic model and data from ENVISION I; simulating clinical remission rate in paediatric ulcerative colitis patients using the Markov exposure-response model and the dosing regimen determined to provide similar efficacy to that observed in ENVISION I.

RESULTS

Both developed population pharmacokinetic and pharmacokinetic/pharmacodynamic models adequately described the observed data. Adalimumab exposure was identified as a significant predictor of clinical remission at Week 8 based on logistic regression [p <0.01]. Simulated efficacy suggested that the fixed-dosing regimen performs similarly to the more efficacious dosing regimen used in ENVISION I, by providing comparable clinical remission per Partial Mayo Score response rates over time. No relationship between adalimumab exposure and adverse events was identified.

CONCLUSIONS

The population pharmacokinetic/pharmacodynamic model supports the appropriateness of the use of the fixed-dosing regimen in the paediatric ulcerative colitis population.

Analysis of Longitudinal-Ordered Categorical Data for Muscle Spasm Adverse Event of Vismodegib: Comparison Between Different Pharmacometric Models

Longitudinal‐ordered categorical data, common in clinical trials, can be effectively analyzed with nonlinear mixed effect models. In this article, we systematically evaluated the performance of three different models in longitudinal muscle spasm adverse event (AE) data obtained from a clinical trial for vismodegib: a proportional odds (PO) model, a discrete‐time Markov model, and a continuous‐time Markov model. All models developed based on weekly spaced data can reasonably capture the proportion of AE grade over time; however, the PO model overpredicted the transition frequency between grades and the cumulative probability of AEs. The influence of data frequency (daily, weekly, or unevenly spaced) was also investigated. The PO model performance reduced with increased data frequency, and the discrete‐time Markov model failed to describe unevenly spaced data, but the continuous‐time Markov model performed consistently well. Clinical trial simulations were conducted to illustrate the muscle spasm resolution time profile during the 8‐week dose interruption period after 12 weeks of continuous treatment.

A Physiologically Based Pharmacokinetic Model for Pregnant Women to Predict the Pharmacokinetics of Drugs Metabolized Via Several Enzymatic Pathways

BACKGROUND

Physiologically based pharmacokinetic modeling is considered a valuable tool for predicting pharmacokinetic changes in pregnancy to subsequently guide in-vivo pharmacokinetic trials in pregnant women. The objective of this study was to extend and verify a previously developed physiologically based pharmacokinetic model for pregnant women for the prediction of pharmacokinetics of drugs metabolized via several cytochrome P450 enzymes.

METHODS

Quantitative information on gestation-specific changes in enzyme activity available in the literature was incorporated in a pregnancy physiologically based pharmacokinetic model and the pharmacokinetics of eight drugs metabolized via one or multiple cytochrome P450 enzymes was predicted. The tested drugs were caffeine, midazolam, nifedipine, metoprolol, ondansetron, granisetron, diazepam, and metronidazole. Pharmacokinetic predictions were evaluated by comparison with in-vivo pharmacokinetic data obtained from the literature.

RESULTS

The pregnancy physiologically based pharmacokinetic model successfully predicted the pharmacokinetics of all tested drugs. The observed pregnancy-induced pharmacokinetic changes were qualitatively and quantitatively reasonably well predicted for all drugs. Ninety-seven percent of the mean plasma concentrations predicted in pregnant women fell within a twofold error range and 63% within a 1.25-fold error range. For all drugs, the predicted area under the concentration-time curve was within a 1.25-fold error range.

CONCLUSION

The presented pregnancy physiologically based pharmacokinetic model can quantitatively predict the pharmacokinetics of drugs that are metabolized via one or multiple cytochrome P450 enzymes by integrating prior knowledge of the pregnancy-related effect on these enzymes. This pregnancy physiologically based pharmacokinetic model may thus be used to identify potential exposure changes in pregnant women a priori and to eventually support informed decision making when clinical trials are designed in this special population.

Sirolimus exposure and the occurrence of cytomegalovirus DNAemia after allogeneic hematopoietic stem cell transplantation

Sirolimus appears to protect against cytomegalovirus (CMV) in organ transplant recipients. The effect of this drug in allogeneic hematopoietic stem cell transplantation recipients remains unexplored. By means of multivariate continuous-time Markov model analyses, we identified 3 independent covariates that significantly impacted the risk of CMV DNAemia: recipient/donor CMV serostatus, tacrolimus exposure, and sirolimus exposure. CMV-seropositive recipients with CMV-seronegative donors had a significantly higher probability of having detectable CMV DNAemia. Increasing the tacrolimus trough concentration from 0 to 16 ng/mL increased the probability of patients having detectable CMV DNAemia by 40% (from 40% to 80%), whereas this probability decreased by 25% (from 40% to 15%) when trough concentrations of sirolimus increased from 0 to 16 ng/mL. Sensitivity analysis showed that sirolimus exposure between 0 and 6 ng/mL has no or negligible effect on CMV DNAemia, but levels >8 ng/mL significantly decreased the number of detectable CMV DNAemia cases (the risk ratios decreased from 0.68 to 0.21 when whole blood sirolimus concentrations changed from 8 to 18 ng/mL, P < .01). In conclusion, we used a pharmacometric statistical tool to provide the first clinical evidence that fewer CMV DNAemia events become detectable as sirolimus exposure increases.

Optimization of maintenance therapy of Risperidone with CYP2D6 genetic polymorphisms through an extended translational framework-based prediction of target occupancies/clinical outcomes

Risperidone, one of the second-generation antipsychotics, can efficiently target dopamine D₂ and serotonin 5-HT_2A receptors. There actually exists significant implication of CYP2D6 genetic polymorphisms on the metabolic kinetics of risperidone, little is known about the extent of CYP2D6 impacting human D2 and 5-HT2A receptor occupancies as well as the clinical efficacy and efficacy in schizophrenia treatment. Here we assessed the influences of CYP2D6 gene polymorphisms on human target occupancies/clinical outcomes and optimized the maintenance therapy of risperidone. A translational framework, previously developed using in vitro and in vivo information in rats, was used as the basis for integrating the effects of CYP2D6 genetic polymorphisms on target occupancies and clinical outcomes. D₂ occupancy as a biomarker was related to Positive and Negative Syndrome Scale (PANSS) response and Simpson-Angus Scale (SAS). The population approach was applied to characterize pharmacokinetic and pharmacodynamic (PK/PD) profiles of risperidone. Non-compartment analysis method was performed to calculate the steady state PK/PD parameters of both risperidone and 9-hydroxyrisperidone. The predictive power of this extended translational framework was determined by comparing the predictions of target occupancies and clinical outcomes with the reported human values of risperidone at clinically suggested dosage of 4.0 mg/day. This extended translational framework was adequately used to predict human target occupancies and clinical outcomes. At the steady state, D₂ ROs were 75.8%, 79.3% and 86.0% for CYP2D6 poor metabolizer (PM), intermediate metabolizer (IM) and extensive metabolizer (EM), respectively; 5-HT_2A ROs were 96.4%, 97.2% and 98.4% for CYP2D6 PM, IM and EM, respectively; PANSS changes from placebo were -5.3, -7.7 and -11.3 for CYP2D6 PM, IM and EM, respectively; SAS changes from placebo were 0.13, 0.15 and 0.18 for CYP2D6 PM, IM and EM, respectively. The predictions of human D₂, 5-HT_2A RO, PANSS and SAS changes for risperidone with CYP2D6 genetic polymorphisms were well in line with the reported values in clinic. 5.0, 4.0 and 2.5 mg/day were the equivalent dosages of risperidone for CYP2D6 PM, IM and EM, respectively. The optimized maintenance therapy of risperidone was provided through the Three-Step method and the dosage range was 2.5-5.0 mg/day for three CYP2D6 gene groups in the present study. Taken together, our findings demonstrate that this extended translational framework not only differentiates the effects of CYP2D6 genetic polymorphisms on target occupancies and clinical outcomes, but also constitutes a scientific basis to optimize the maintenance therapy of neuropsychiatric patients in clinic.

Physiologically Based Pharmacokinetic Modeling of Renally Cleared Drugs in Pregnant Women

BACKGROUND

Since pregnant women are considerably underrepresented in clinical trials, information on optimal dosing in pregnancy is widely lacking. Physiologically based pharmacokinetic (PBPK) modeling may provide a method for predicting pharmacokinetic changes in pregnancy to guide subsequent in vivo pharmacokinetic trials in pregnant women, minimizing associated risks.

OBJECTIVES

The goal of this study was to build and verify a population PBPK model that predicts the maternal pharmacokinetics of three predominantly renally cleared drugs (namely cefazolin, cefuroxime, and cefradine) at different stages of pregnancy. It was further evaluated whether the fraction unbound (f _u) could be estimated in pregnant women using a proposed scaling approach.

METHODS

Based on a recent literature review on anatomical and physiological changes during pregnancy, a pregnancy population PBPK model was built using the software PK-Sim^®/MoBi^®. This model comprised 27 compartments, including nine pregnancy-specific compartments. The PBPK model was verified by comparing the predicted maternal pharmacokinetics of cefazolin, cefuroxime, and cefradine with observed in vivo data taken from the literature. The proposed scaling approach for estimating the f _u in pregnancy was evaluated by comparing the predicted f _u with experimentally observed f _u values of 32 drugs taken from the literature.

RESULTS

The pregnancy population PBPK model successfully predicted the pharmacokinetics of cefazolin, cefuroxime, and cefradine at all tested stages of pregnancy. All predicted plasma concentrations fell within a 2-fold error range and 85% of the predicted concentrations within a 1.25-fold error range. The f _u in pregnancy could be adequately predicted using the proposed scaling approach, although a slight underestimation was evident in case of drugs bound to α₁-acidic glycoprotein.

CONCLUSION

Pregnancy population PBPK models can provide a valuable tool to predict a priori the pharmacokinetics of predominantly renally cleared drugs in pregnant women. These models can ultimately support informed decision making regarding optimal dosing regimens in this vulnerable special population.

A continuous-time multistate Markov model to describe the occurrence and severity of diarrhea events in metastatic breast cancer patients treated with lumretuzumab in combination with pertuzumab and paclitaxel

PURPOSE

To inform lumretuzumab and pertuzumab dose modifications in order to decrease the incidence, severity, and duration of the diarrhea events in metastatic breast cancer patients treated with a combination therapy of lumretuzumab (anti-HER3) in combination with pertuzumab (anti-HER2) and paclitaxel using quantitative clinical pharmacology modeling approaches.

METHODS

The safety and pharmacokinetic (PK) data from three clinical trials (lumretuzumab monotherapy n = 47, pertuzumab monotherapy n = 78, and the combination therapy of lumretuzumab, pertuzumab and paclitaxel n = 35) were pooled together to develop a continuous-time discrete states Markov model describing the dynamics of the diarrhea events.

RESULTS

The model was able to capture the time course of different severities of diarrhea reasonably well. The effect of lumretuzumab and pertuzumab was well described by an E_max function indicating an increased rate of transition from moderate to mild or more severe diarrhea with higher doses. The concentration needed to trigger or worsen diarrhea episodes was estimated to be 120-fold lower in combination therapy compared to monotherapy, suggesting strong synergy between the two monoclonal antibodies. The prophylactic effect of loperamide in a subset of patients was also well captured by the model with a clear tendency to reduce the occurrence of diarrhea events.

CONCLUSIONS

This work shows that PK-toxicity modeling provides insight into how the severity of key adverse events evolves over time and highlights the potential use to support decision making in drug development.

Effects of prophylactic anticholinergic medications to decrease extrapyramidal side effects in patients taking acute antiemetic drugs: a systematic review and meta-analysis

OBJECTIVES

To determine the effectiveness of prophylactic anticholinergic medications in reducing extrapyramidal symptoms in patients taking acute antiemetics with a dopamine D2 receptor antagonist effect.

METHODS

Systematic searches of all published studies through March 2017 were identified from PubMed, Cochrane library, Embase, Web of Science and Scopus. Only randomised controlled trials of patients receiving dopamine D2 antagonist antiemetic therapy for acute migraine in which an anticholinergic or placebo was compared were included. Pooled ORs were calculated for incidence of extrapyramidal symptoms and sedation.

RESULTS

Four placebo-controlled randomised controlled trials consisting of 737 patients met the inclusion criteria for our meta-analysis. The effect of diphenhydramine differed depending on the method of administration of the antiemetic. When the antiemetic was delivered as a 2 min antiemetic bolus, the odds of extrapyramidal symptoms were significantly reduced in the diphenhydramine group compared with placebo (OR 0.42; 95% CI 0.22 to 0.81; P=0.01). However, when the antiemetic was given as a 15 min infusion, there was no significant difference in extrapyramidal symptoms with or without diphenhydramine (OR 1.06; 95% CI 0.58 to 1.91; P=0.85). The lowest incidence of extrapyramidal symptoms was observed in patients receiving a 15 min antiemetic infusion without diphenhydramine prophylaxis (9.8%). In two trials including 351 patients that dichotomously reported sedation scales, diphenhydramine had significantly higher rates of sedation (31.6%vs19.2%, OR 2.01, 95% CI 1.21 to 3.33; P=0.007).

CONCLUSION

Prophylactic diphenhydramine reduces extrapyramidal symptoms in patients receiving bolus antiemetic therapy with a dopamine D2 antagonist effect, but not when it is given as an infusion. Because of significantly greater sedation with diphenhydramine, the most effective strategy is to administer the D2 antagonist antiemetic as a 15 min infusion without prophylaxis.

A Minimal Continuous-Time Markov Pharmacometric Model

In this work, an alternative model to discrete-time Markov model (DTMM) or standard continuous-time Markov model (CTMM) for analyzing ordered categorical data with Markov properties is presented: the minimal CTMM (mCTMM). Through a CTMM reparameterization and under the assumption that the transition rate between two consecutive states is independent on the state, the Markov property is expressed through a single parameter, the mean equilibration time, and the steady-state probabilities are described by a proportional odds (PO) model. The mCTMM performance was evaluated and compared to the PO model (ignoring Markov features) and to published Markov models using three real data examples: the four-state fatigue and hand-foot syndrome data in cancer patients initially described by DTMM and the 11-state Likert pain score data in diabetic patients previously analyzed with a count model including Markovian transition probability inflation. The mCTMM better described the data than the PO model, and adequately predicted the average number of transitions per patient and the maximum achieved scores in all examples. As expected, mCTMM could not describe the data as well as more flexible DTMM but required fewer estimated parameters. The mCTMM better fitted Likert data than the count model. The mCTMM enables to explore the effect of potential predictive factors such as drug exposure and covariates, on ordered categorical data, while accounting for Markov features, in cases where DTMM and/or standard CTMM is not applicable or conveniently implemented, e.g., non-uniform time intervals between observations or large number of categories.

A Modeling and Simulation Framework for Adverse Events in Erlotinib-Treated Non-Small-Cell Lung Cancer Patients

Treatment with erlotinib, an epidermal growth factor receptor tyrosine kinase inhibitor used for treating non-small-cell lung cancer (NSCLC) and other cancers, is frequently associated with adverse events (AE). We present a modeling and simulation framework for the most common erlotinib-induced AE, rash, and diarrhea, providing insights into erlotinib toxicity. We used the framework to investigate the safety of high-dose erlotinib pulses proposed to limit acquired resistance while treating NSCLC. Continuous-time Markov models were developed using rash and diarrhea AE data from 39 NSCLC patients treated with erlotinib (150 mg/day). Exposure and different covariates were investigated as predictors of variability. Rash was also tested as a survival predictor. Models developed were used in a simulation analysis to compare the toxicities of different regimens, including the previously mentioned pulsed strategy. Probabilities of experiencing rash or diarrhea were found to be highest early during treatment. Rash, but not diarrhea, was positively correlated with erlotinib exposure. In contrast with some common understandings, radiotherapy decreased transitioning to higher rash grades by 81% (p < 0.01), and experiencing rash was not correlated with positive survival outcomes. Model simulations predicted that the proposed pulsed regimen (1600 mg/week + 50 mg/day remaining week days) results in a maximum of 20% of the patients suffering from severe rash throughout the treatment course in comparison to 12% when treated with standard dosing (150 mg/day). In conclusion, the framework demonstrated that radiotherapy attenuates erlotinib-induced rash, providing an opportunity to use radiotherapy and erlotinib together, and demonstrated the tolerability of high-dose pulses intended to address acquired resistance to erlotinib.

Optimal sampling of antipsychotic medicines: a pharmacometric approach for clinical practice

AIM

To determine optimal sampling strategies to allow the calculation of clinical pharmacokinetic parameters for selected antipsychotic medicines using a pharmacometric approach.

METHODS

This study utilized previous population pharmacokinetic parameters of the antipsychotic medicines aripiprazole, clozapine, olanzapine, perphenazine, quetiapine, risperidone (including 9-OH risperidone) and ziprasidone. d-optimality was utilized to identify time points which accurately predicted the pharmacokinetic parameters (and expected error) of each drug at steady-state. A standard two stage population approach (STS) with MAP-Bayesian estimation was used to compare area under the concentration-time curves (AUC) generated from sparse optimal time points and rich extensive data. Monte Carlo Simulation (MCS) was used to simulate 1000 patients with population variability in pharmacokinetic parameters. Forward stepwise regression analysis was used to determine the most predictive time points of the AUC for each drug at steady-state.

RESULTS

Three optimal sampling times were identified for each antipsychotic medicine. For aripiprazole, clozapine, olanzapine, perphenazine, risperidone, 9-OH risperidone, quetiapine and ziprasidone the CV% of the apparent clearance using optimal sampling strategies were 19.5, 8.6, 9.5, 13.5, 12.9, 10.0, 16.0 and 10.7, respectively. Using the MCS and linear regression approach to predict AUC, the recommended sampling windows were 16.5-17.5 h, 10-11 h, 23-24 h, 19-20 h, 16.5-17.5 h, 22.5-23.5 h, 5-6 h and 5.5-6.5 h, respectively.

CONCLUSION

This analysis provides important sampling information for future population pharmacokinetic studies and clinical studies investigating the pharmacokinetics of antipsychotic medicines.

Simultaneous Exposure-Response Modeling of ACR20, ACR50, and ACR70 Improvement Scores in Rheumatoid Arthritis Patients Treated With Certolizumab Pegol

The Markovian approach has been proposed to model American College of Rheumatology's (ACR) response (ACR20, ACR50, or ACR70) reported in rheumatoid arthritis clinical trials to account for the dependency of the scores over time. However, dichotomizing the composite ACR assessment discards much information. Here, we propose a new approach for modeling together the three thresholds: a continuous-time Markov exposure–response model was developed, based on data from five placebo-controlled certolizumab pegol clinical trials. This approach allows adequate prediction of individual ACR20/50/70 time-response, even for non-periodic observations. An exposure–response was established over a large range of licensed and unlicensed doses including phase II dose-ranging data. Simulations from the model (50–400 mg every other week) illustrated the range and sustainability of response (ACR20: 56–68%, ACR50: 27–42%, ACR70: 11–22% at week 24) with maximum clinical effect achieved at the recommended maintenance dose of 200 mg every other week.

Dopamine D2 receptor occupancy as a predictor of catalepsy in rats: a pharmacokinetic-pharmacodynamic modeling approach

OBJECTIVES

Dopamine D2 receptor occupancy (D2RO) is the major determinant of efficacy and safety in schizophrenia drug therapy. Excessive D2RO (>80%) is known to cause catalepsy (CAT) in rats and extrapyramidal side effects (EPS) in human. The objective of this study was to use pharmacokinetic and pharmacodynamic modeling tools to relate CAT with D2RO in rats and to compare that with the relationship between D2RO and EPS in humans.

METHODS

Severity of CAT was assessed in rats at hourly intervals over a period of 8 h after antipsychotic drug treatment. An indirect response model with and without Markov elements was used to explain the relationship of D2RO and CAT.

RESULTS

Both models explained the CAT data well for olanzapine, paliperidone and risperidone. However, only the model with the Markov elements predicted the CAT severity well for clozapine and haloperidol. The relationship between CAT scores in rat and EPS scores in humans was implemented in a quantitative manner. Risk of EPS not exceeding 10% over placebo correlates with less than 86% D2RO and less than 30% probability of CAT events in rats.

CONCLUSION

A quantitative relationship between rat CAT and human EPS was elucidated and may be used in drug discovery to predict the risk of EPS in humans from D2RO and CAT scores measured in rats.

Cross-membrane signal transduction of receptor tyrosine kinases (RTKs): from systems biology to systems pharmacology

Receptor tyrosine kinases are high-affinity cell surface receptors for many polypeptide growth factors, cytokines, and hormones. They straddle the cell wall and play an important role in cross-membrane signalling. We present a two-component systems pharmacology model based on the local physiology and identify characteristic features of its dynamics. We thus present a transparent tool for studying the effects of drug intervention and ways of administration on cross-membrane signalling through these receptors.

CPT: Pharmacometrics and Systems Pharmacology

Welcome to the first issue of CPT: Pharmacometrics and Systems Pharmacology (CPT:PSP), a new journal from the American Society for Clinical Pharmacology and Therapeutics. CPT:PSP is a cross-disciplinary journal devoted to publishing advances in quantitative, model-based approaches as applied in pharmacology, (patho)physiology, and disease to aid the discovery, development, and utilization of human therapeutics. The emphasis of CPT:PSP will be on the application of modeling and simulation and the impact of Pharmacometrics and Systems Pharmacology on the discovery and development of innovative therapies.