Pharmacokinetic-pharmacodynamic modeling: why?

Pharmacokinetic-Pharmacodynamic Modeling of Midazolam in Pediatric Surgery

Midazolam (MDZ) is used for sedation in surgical procedures; its clinical effect is related to its receptor affinity and the dose administered. Therefore, a pharmacokinetic-pharmacodynamic (PK-PD) population model of MDZ in pediatric patients undergoing minor surgery is proposed. A descriptive, observational, prospective, and longitudinal, study that included patients of both sexes, aged 2-17 years, ASA I/II, who received MDZ in IV doses (0.05 mg/kg) before surgery. Three blood samples were randomly taken between 5-120 min; both sedation by the Bispectral Index Scale (BIS) and its adverse effects were recorded. The PK-PD relationship was determined using a nonlinear mixed-effects, bicompartmental first-order elimination model using Monolix Suite™. Concentrations and the BIS were fitted to the sigmoid Emax PK-PD population and sigmoid Emax PK/PD indirect binding models, obtaining drug concentrations at the effect site (biophase). The relationship of concentrations and BIS showed a clockwise hysteresis loop, probably indicating time-dependent protein binding. Of note, at half the dose used in pediatric patients, adequate sedation without adverse effects was demonstrated. Further PK-PD studies are needed to optimize dosing schedules and avoid overdosing or possible adverse effects.

Long-acting microneedle formulations

The minimally-invasive and painless nature of microneedle (MN) application has enabled the technology to obviate many issues with injectable drug delivery. MNs not only administer therapeutics directly into the dermal and ocular space, but they can also control the release profile of the active compound over a desired period. To enable prolonged delivery of payloads, various MN types have been proposed and evaluated, including dissolving MNs, polymeric MNs loaded or coated with nanoparticles, fast-separable MNs hollow MNs, and hydrogel MNs. These intricate yet intelligent delivery platforms provide an attractive approach to decrease side effects and administration frequency, thus offer the potential to increase patient compliance. In this review, MN formulations that are loaded with various therapeutics for long-acting delivery to address the clinical needs of a myriad of diseases are discussed. We also highlight the design aspects, such as polymer selection and MN geometry, in addition to computational and mathematical modeling of MNs that are necessary to help streamline and develop MNs with high translational value and clinical impact. Finally, up-scale manufacturing and regulatory hurdles along with potential avenues that require further research to bring MN technology to the market are carefully considered. It is hoped that this review will provide insight to formulators and clinicians that the judicious selection of materials in tandem with refined design may offer an elegant approach to achieve sustained delivery of payloads through the simple and painless application of a MN patch.

Comparation of time-course, dose-effect, influencing factors and adverse events of biologics in the treatment of adults with moderate to severe plaque psoriasis

Introduction

Biologics is used for treating moderate to severe plaque psoriasis (MSPP), which represent one of the foremost therapeutic advancements in disease of dermatology. Up to now, the relative efficacy and safety across approved andinvestigational biologics for MSPP is still unclear.

Methods

This study aimed to comparative effectiveness of various biological treatments for MSPP measured by PASI75, PASI90 and PASI100 (The ratio of patients whose Psoriasis Area and Severity Index score (PASI) decreased by ≥ 75%, 90% and 100% compared with baseline, respectively). In addition, random models were used together with a Bayesian method to compare direct and indirect Adverse Events (AEs) of biologics with placebo, to make probabilistic statements and predictions on their AEs. The analytic data set was made up of summarized data from 54 trials, including 27,808 patients, with treatment of 17 biologics. Three mathematic models with nonparametric placebo evaluations were established to characterize the longitudinal direction profile for the three efficacy measures as above mentioned.

Results

Our results showed significant differences among treatments. Bimekizumab, sonelokimab, and ixekizumab were found to be the most effective treatments among the biologics. The effects of covariate were further evaluated, patients' age, body weight, duration of disease and percentage of patients previously treated with a biological therapy showed impact on the efficacy. In addition, we found that ixekizumab and risankizumab displayed relatively stable as for efficacy and safety.

Discussion

Our findings provide valuable insights into the comparative effectiveness and safety of biologics for MSPP treatment. These results may aid in clinical decision-making and ultimately improve patient outcomes.

Pharmacokinetic-pharmacodynamic (PK/PD) modeling to study the hepatoprotective effect of Perilla Folium on the acute hepatic injury rats

ETHNOPHARMACOLOGICAL RELEVANCE

Perilla Folium (PF), is a traditional medicinal material with the homology of medicine and food in China and has been widely used due to its rich nutritional content and medicinal value. The hepatoprotective effects of PF extract include their protection against acute hepatic injury, tert-butylhydroperoxide (t-BHP) induced oxidative damage, and Lipopolysaccharide (LPS) and D-galactosamine (D-GalN) induced hepatic injury have been well studied. However, there are few reports on the pharmacokinetics studies of PF extract in acute hepatic injury model rats, and the anti-hepatic injury activity of PF is still unclear.

AIM OF THE STUDY

The differences in the plasma pharmacokinetic of 21 active compounds between the normal and model groups were compared， and established pharmacokinetics/pharmacodynamics (PK/PD) modeling was to analyze the hepatoprotective effects of PF.

MATERIALS AND METHODS

The acute hepatic injury model was induced with an intraperitoneal injection of lipopolysaccharide (LPS) and D-galactosamine (D-GalN), and the plasma pharmacokinetics of 21 active compounds of PF were analyzed in the normal and model groups using ultra-high performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS). The correlation between plasma components and hepatoprotective effects indicators (the alanine aminotransferase (ALT), aspartate aminotransferase (AST) and lactic dehydrogenase (LDH)) in the model group was also investigated and established a Pharmacokinetic/pharmacodynamic (PK/PD) correlation analysis of the hepatoprotective effects of PF.

RESULTS

The results revealed that organic acid compounds possessed the characteristics of faster absorption, shorter peak time and slower metabolism, while the flavonoid compounds had slower absorption and longer peak time, and the pharmacokinetics of various components were significantly affected after modeling. The results of PK/PD modeling analysis demonstrated that the plasma drug concentration of each component existed a good correlation with the three AST, ALT, and LDH, and the lag time of the efficacy of each component is relatively long.

CONCLUSIONS

The plasma drug concentration of each component existed a good correlation with the three AST, ALT, and LDH, and the lag time of the efficacy of each component is relatively long in vivo.

Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review

Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.

Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.

Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System

Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales. Markov chains are used to model the cell behaviors by taking into account the gene expression levels, cell cycle, and the microenvironment. The proposed model enables the prediction of tumor growth under given molecular properties, microenvironment conditions, and drug PK/PD profile. Simulation results demonstrate the effectiveness of the proposed approach and the results agree with observed tumor behaviors.

Translational Research in Oncology: Implications for Palliative Care

The label "translational research" (TR) has become ever more popular in the biomedical domain in recent years. It is usually presented as an attempt to bridge a supposed gap between knowledge produced at the laboratory bench and its use at the clinical bedside. This is claimed to help society harvest the benefits of its investments in scientific research. The past decade has witnessed a remarkable acceleration in the pace of translational cancer medicine - genome sequencing of various human cancers has been broadly deployed in drug discovery programs, diagnostic tests have been developed to predict molecularly targeted anticancer agents, advent of cancer immunotherapies, an enhanced appreciation of the complex interactions that exist between tumor cells and their microenvironment have revolutionized the management of cancers. Treatment for cancer and palliative care (PC) go hand in hand and the role of TR in PC can no longer be ignored. This paper discusses about the scientific discourse of TR in cancer care and its implications for the practice of PC. It starts with a brief reconstruction of the history of the concept and subsequently unravels how the label is used in clinical/research practice. In conclusion, TR seems to be driven by a changed relationship between science and society. "Translation" has become important because society is thought to deserve a tangible return in terms of health and quality of life on its investment in basic biomedical science.

Bayesian network to optimize the first dose of antibiotics: application to amikacin

Objective: To construct and validate a network to predict the first dose of amikacin. Methods: Anthropometric and therapeutic data were recorded for 120 patients. Bayesian network (BN) was built to predict the dose to achieve a fixed target peak concentration of 64 mg/l. In 40 subjects, doses predicted with the BN (BND) and based on body weight (BWD) were compared with adjusted doses calculated using a pharmacokinetic software (MM-USCPACK; BID). Results: The calculated dose differed by <20% from the ideal dose in 62.5% of the patients with the BN and in 43.8% of the patients with the BW. Conclusion: BN is a promising approach to optimize the prediction of the first dose.

Pharmacokinetics and pharmacodynamics of glycyrrhetinic acid with Paeoniflorin after transdermal administration in dysmenorrhea model mice

BACKGROUND

Glycyrrhetinic acid (GA) and paeoniflorin (PF) are the main active ingredients in Chinese peony- Liquorice Decoction, a widely used Traditional Chinese Medicine.

HYPOTHESIS/PURPOSE

The aim of this work was to investigate the combinatory analgesic effect of GA and PF after percutaneous administration and to define their pharmacokinetic/pharmacodynamic (PK/PD) characteristics.

STUDY DESIGN AND METHODS

GA and PF were produced to transdermal patches based on previous research, and the permeation parameters of GA and PF in the patches were investigated with in vitro experiments. Dysmenorrhea model mice were then produced to compare the analgesic effects of the patches with different proportions of GA-PF. In the in vivo assessment, the number of writhes exhibited by the dysmenorrhea mice was recorded at designated time points, and skin, muscle under skin and plasma samples were collected, for assessments of drug distribution, pharmacokinetics parameters and PK/PD characteristics.

RESULTS AND CONCLUSION

In dysmenorrhea mice, GA-PF and meloxicam (the positive control drug) could relieve pain to equal degrees. Specifically, a single dose of the optimized patches (10%GA-10%PF, wt) exerted a steady analgesic effect for 48h in dysmenorrhea mice, but this effect lagged behind the changes in the plasma concentration. Evaluation with the Bliss Independence criterion revealed that the two ingredients displayed a synergistic effect. Then the PK/PD relationship of GA in this compound preparation was defined with this synergistic effect. The preparation might be suitable for topical spasmolysis and anti-inflammatory therapy.

Modeling for critically ill patients: An introduction for beginners

Models are mathematical tools used to describe real-world features. Therapeutic interventions in the field of critical care medicine may easily be translated into such models. Indeed, numerous variables influencing drug pharmacokinetics and pharmacodynamics are systematically documented in the intensive care unit over time. Organ failure, fluid shifts, other drug administration, and renal replacement therapy may cause changes in physiological values, such as body weight and composition, temperature, serum protein levels, arterial pH, and renal or hepatic function. Trials assessing the efficacy and safety of novel drugs usually exclude critically ill patients, and guidelines regarding drug dosage rarely apply to such patients. Modeling in the critically ill may allow physicians to inform decisions related to therapeutic interventions, particularly relating to infectious diseases. However, few clinicians are familiar with these methods. Here, we present a current overview of population pharmacokinetic and pharmacodynamic models applicable in critically ill patients aimed at nonspecialists and then emphazize recent potential of modeling for optimizing treatments and care in the intensive care unit.

Pharmacokinetic-pharmacodynamic relationships of macitentan, a new endothelin receptor antagonist, after multiple dosing in healthy Korean subjects

BACKGROUND AND OBJECTIVES

Macitentan is a novel dual endothelin (ET)-1 receptor antagonist to be used in patients with pulmonary arterial hypertension. This study aimed to assess the pharmacokinetics (PK) and pharmacodynamics (PD) of macitentan after administration of multiple doses to healthy Korean male subjects.

METHODS

A randomized, double-blind, placebo-controlled, multiple-ascending dose study was performed in 30 healthy male subjects receiving oral macitentan (3, 10, or 30 mg) or placebo once daily for 10 days. Plasma concentrations of macitentan, its active metabolite ACT-13277, and ET-1 were evaluated. Safety and tolerability measurements were conducted throughout the study.

RESULTS

The concentration-time profile of macitentan was characterized by slow absorption (median time to maximum plasma concentration [t(max)] 9-10 h) and slow elimination (mean elimination half-life [t ½] 11-15 h). After repeated doses of 3, 10, and 30 mg of macitentan over the course of 10 days, the peak concentration (C(max)) increased as the dose increased and the area under the plasma concentration-time curve during the dosing interval (AUC(τ)) increased in a dose-proportional manner. Plasma concentrations showed approximately 1.5- to 1.9-fold accumulation on day 10 compared with day 1. ACT-132577 showed higher levels of exposure than macitentan, its mean half-life was 46-48 h, and it accumulated 7- to 12-fold. Macitentan increased plasma ET-1 concentrations at all doses tested and was well tolerated and elicited no serious adverse events.

CONCLUSION

Multiple oral doses of 3, 10, and 30 mg of macitentan were well tolerated in healthy Korean subjects, and its pharmacokinetics correlated positively with ET-1 concentrations.

Compartmental pharmacokinetic modeling of lopinavir in humans

BACKGROUND

Lopinavir is a highly potent protease inhibitors commonly used in treatment of HIV infection. The drug has a very low bioavailability due to a rapid metabolism by cytochrome P450 3A (CYP3A) isoenzyme. We aimed to develop a biologically relevant pharmacokinetic model of lopinavir with a description of a CYP3A4-mediated first pass metabolism and enterohepatic circulation (EHC).

METHODS

A theoretical model of lopinavir was developed using the classical pharmacokinetic modeling concept. The model consisted of one compartment with first-order absorption from gastrointestinal (GI) depot and first-order clearance into recycling depot which incorporated into the model structure using ACSLX.

RESULTS

Lopinavir plasma concentration-time course was successfully simulated against a dataset from the literature. The model had an absorption rate constant (Ka) of 0.991 h(-1), a reabsorption rate constant (K(reabsorb)) of 0.171 h(-1), and a volume of distribution (V/F) of 54.7 L. The 12 hours area under the concentration-time curve (AUC0-12h) values from our model simulation compares to the experimental data were 0.8141 µg/ml.h and 0.7058 µg/ml.h, respectively. Maximum plasma concentration of the drug (Cmax) predicted from our model compare to the experimental data were 0.273 µg/ml and 0.442 µg/ml, respectively. While, minimum plasma concentration of the drug (C(trough)) predicted from our model compare to the experimental data were similar at 0.0015 µg/ml.

CONCLUSIONS

Modified one compartment with first-order absorption from gastrointestinal (GI) depot and first-order clearance into recycling depot describes a pharmacokinetic of oral single dose of lopinavir 400 mg. The model can also simulate a concentration-time course with a difference dosing and variable which can be used for further describing the pharmacology of the drug interaction when combine with the other drugs.

Dynamical modeling of drug effect using hybrid systems

: Drug discovery today is a complex, expensive, and time-consuming process with high attrition rate. A more systematic approach is needed to combine innovative approaches in order to lead to more effective and efficient drug development. This article provides systematic mathematical analysis and dynamical modeling of drug effect under gene regulatory network contexts. A hybrid systems model, which merges together discrete and continuous dynamics into a single dynamical model, is proposed to study dynamics of the underlying regulatory network under drug perturbations. The major goal is to understand how the system changes when perturbed by drugs and give suggestions for better therapeutic interventions. A realistic periodic drug intake scenario is considered, drug pharmacokinetics and pharmacodynamics information being taken into account in the proposed hybrid systems model. Simulations are performed using MATLAB/SIMULINK to corroborate the analytical results.

A Systems Biology Approach in Therapeutic Response Study for Different Dosing Regimens-a Modeling Study of Drug Effects on Tumor Growth using Hybrid Systems

Motivated by the frustration of translation of research advances in the molecular and cellular biology of cancer into treatment, this study calls for cross-disciplinary efforts and proposes a methodology of incorporating drug pharmacology information into drug therapeutic response modeling using a computational systems biology approach. The objectives are two fold. The first one is to involve effective mathematical modeling in the drug development stage to incorporate preclinical and clinical data in order to decrease costs of drug development and increase pipeline productivity, since it is extremely expensive and difficult to get the optimal compromise of dosage and schedule through empirical testing. The second objective is to provide valuable suggestions to adjust individual drug dosing regimens to improve therapeutic effects considering most anticancer agents have wide inter-individual pharmacokinetic variability and a narrow therapeutic index. A dynamic hybrid systems model is proposed to study drug antitumor effect from the perspective of tumor growth dynamics, specifically the dosing and schedule of the periodic drug intake, and a drug’s pharmacokinetics and pharmacodynamics information are linked together in the proposed model using a state-space approach. It is proved analytically that there exists an optimal drug dosage and interval administration point, and demonstrated through simulation study.

Enhanced analgesic responses after preferential delivery of morphine and fentanyl to the olfactory epithelium in rats

BACKGROUND

Centrally acting opioid analgesics such as morphine and fentanyl are effective, but their efficacy is often limited by a delayed response or side effects resulting from systemic first pass before reaching the brain and the central nervous system (CNS). It is generally accepted that drugs applied to the nasal cavity can directly access the brain and the CNS, which could provide therapeutic advantages such as rapid onset and lower systemic exposure. The olfactory region of the nasal cavity has been implicated in facilitating this direct nose-to-CNS transfer. If the fraction of opioid administered to the olfactory region could be improved, there could be a larger fraction of drug directly delivered to the CNS, mediating greater therapeutic benefit.

METHODS

We have developed a pressurized olfactory delivery (POD) device to consistently and noninvasively deposit a majority of drug on the olfactory region of the nasal cavity in Sprague-Dawley rats. Using the tail-flick latency test and analysis of plasma and CNS tissue drug exposure, we compared distribution and efficacy of the opioids morphine and fentanyl administered to the nasal olfactory region with the POD device or the nasal respiratory region with nose drops or systemically via intraperitoneal injection.

RESULTS

Compared with nose drop administration, POD administration of morphine resulted in a significantly higher overall therapeutic effect (area under the curve [over the time course] [AUC](effect)) without a significant increase in plasma drug exposure (AUC(plasma)). POD of morphine resulted in a nose-to-CNS direct transport percentage of 38% to 55%. POD of fentanyl led to a faster (5 vs 10 minutes) and more intense analgesic effect compared with nasal respiratory administration. Unlike intraperitoneal injection or nose drop administration, both morphine and fentanyl given by the POD device to olfactory nasal epithelium exhibited clockwise (plasma) versus effect hysteresis after nasal POD administration, consistent with a direct nose-to-CNS drug transport mechanism.

CONCLUSIONS

Deposition of opioids to the olfactory region within the nasal cavity could have a significant impact on drug distribution and pharmacodynamic effect, and thus should be considered in future nasally administered opioid studies.

Integration of in silico and in vitro platforms for pharmacokinetic-pharmacodynamic modeling

IMPORTANCE OF THE FIELD

Pharmacokinetic-pharmacodynamic (PK-PD) modeling enables quantitative prediction of the dose-response relationship. Recent advances in microscale technology enabled researchers to create in vitro systems that mimic biological systems more closely. Combination of mathematical modeling and microscale technology offers the possibility of faster, cheaper and more accurate prediction of the drug's effect with a reduced need for animal or human subjects.

AREAS COVERED IN THIS REVIEW

This article discusses combining in vitro microscale systems and PK-PD models for improved prediction of drug's efficacy and toxicity. First, we describe the concept of PK-PD modeling and its applications. Different classes of PK-PD models are described. Microscale technology offers an opportunity for building physical systems that mimic PK-PD models. Recent progress in this approach during the last decade is summarized.

WHAT THE READER WILL GAIN

This article is intended to review how microscale technology combined with cell cultures, also known as 'cells-on-a-chip', can confer a novel aspect to current PK-PD modeling. Readers will gain a comprehensive knowledge of PK-PD modeling and 'cells-on-a-chip' technology, with the prospect of how they may be combined for synergistic effect.

TAKE HOME MESSAGE

The combination of microscale technology and PK-PD modeling should contribute to the development of a novel in vitro/in silico platform for more physiologically-realistic drug screening.

A micro cell culture analog (microCCA) with 3-D hydrogel culture of multiple cell lines to assess metabolism-dependent cytotoxicity of anti-cancer drugs

A microfluidic device with 3-D hydrogel cell cultures has been developed to test the cytotoxicity of anti-cancer drugs while reproducing multi-organ interactions. In this device, a micro cell culture analog (microCCA), cells embedded in 3-D hydrogels are cultured in separate chambers representing the liver, tumor, and marrow, which are connected by channels mimicking blood flow. While the microfluidic network provides a platform for mimicking the pharmacokinetic and pharmacodynamic profiles of a drug in humans, the 3-D hydrogel provides a more physiologically realistic environment to mimic the tissue than monolayer culture. Colon cancer cells (HCT-116) and hepatoma cells (HepG2/C3A) were encapsulated in Matrigel and cultured in the tumor and the liver chamber in a microCCA, respectively. Myeloblasts (Kasumi-1) were encapsulated in alginate in the marrow chamber; a stiffer hydrogel was necessary to prevent cell migration out of the matrix. The cytotoxic effect of Tegafur, an oral prodrug of 5-fluorouracil (5-FU), on each cell line was tested using the microCCA with cell-embedded hydrogel. The comparison of experimental results using a 96-well microtiter plate and a microCCA demonstrated that the microCCA was able to reproduce the metabolism of Tegafur to 5-FU in the liver and consequent death of cells by 5-FU, while the cultures in a 96-well microtiter plate were unable to do so. The microCCA utilizing 3-D hydrogel cell cultures has potential as a platform for pharmacokinetic-based drug screening in a more physiologically realistic environment.

Using pharmacokinetic-pharmacodynamic modelling as a tool for prediction of therapeutic effective plasma levels of antipsychotics

In the rat, selective suppression of conditioned avoidance response has been widely reported as a test with high predictive validity for antipsychotic efficacy. Recent studies have shown that the relationship between dopamine D2 receptor occupancy and the suppression of conditioned avoidance response behaviour correlates well with the relationship between human dopamine D2 receptor occupancy and clinical effect. The aim of the present study was to evaluate how pharmacokinetic/pharmacodynamic (PK/PD) predictions of therapeutic effective steady-state plasma levels by means of conditioned avoidance response behaviour in rodents, correlate with clinically relevant plasma exposure for the classical antipsychotic drug haloperidol and four second generation antipsychotics: sertindole, clozapine, risperidone and olanzapine, including selected metabolites. In order to confirm the validity of the present conditioned avoidance response procedure, in vivo striatal dopamine D2 receptor occupancy was determined in parallel using 3H-raclopride as the radioligand. The PK/PD relationship was established by modelling the time-response and time-plasma concentration data. We found the order of dopamine D2 receptor occupancy required to suppress conditioned avoidance response behaviour according to EC50 measurements to be sertindole (+dehydrosertindole)=dehydrosertindole=paliperidone (the metabolite of risperidone)=haloperidol=olanzapine>risperidone>>clozapine. Overall, a good agreement was observed between the rat dopamine D2 receptor occupancy levels providing 50% response in the conditioned avoidance response test and the dopamine D2 receptor occupancy levels reported from responding schizophrenic patients treated with antipsychotics. Predictions of therapeutically effective steady-state levels for sertindole (+dehydrosertindole) and olanzapine were 3-4-fold too high whereas for haloperidol, clozapine and risperidone the predicted steady-state EC50 in conditioned avoidance responding rats correlated well with the therapeutically effective plasma levels observed in patients. Accordingly, the proposed PK/PD model may act as a guide for determining effective plasma concentrations of potential antipsychotics in the clinical setting and thereby accelerating the overall drug development process.

Contribution of modelling chemotherapy-induced hematological toxicity for clinical practice

Anticancer chemotherapies are responsible for numerous adverse events. Among these, hematological toxicity is one of the main causes for ending treatment. These toxicities decrease production of red blood cells (anemia), production of white blood cells (neutropenia or granulocytopenia), and production of platelets (thrombocytopenia), which may be life-threatening to the patient. Preventing such discontinuation would be valuable for treating patients more effectively. In order to achieve this goal, numerous mathematical and physiological or semiphysiological models have been developed. The complexity of models has increased over the years, from empiric E(max) models to mechanistic models including physiological mechanisms such as feedback control. This review discusses several approaches of modelling hematological toxicities illustrated with some examples: pharmacodynamic models for the hematological toxicity of 5-fluorouracil, epirubicin, melphalan, paclitaxel, topotecan, and indisulam.

A Novel PBPK/PD Model with Automatic Nervous System in Anesthesia

In terms of the characteristic of anesthetic operation, this paper propose new Physiologically Based Pharmacokinetics / Pharmacodynamics (PBPK/PD) model that focus on the important role of Autonomic Nervous System (ANS) more. Automatic nervous system that is divided into the sympathetic nervous system and the parasympathetic nervous system, at the same time, is the important target of drug effect in anesthesia. Consequently, Not only does the action levels of Automatic Nervous System indicator the Anesthesia State, but feed back altering the physiological state parameters of pharmacokinetics, which being one crucial reason of time-variant and nonlinear drug effect. However, the recent PBPK modeling and simulation strategy only estimate specific absorption, distribution, metabolism and excretion (ADME) parameters but consider no modulation result of Automatic Nervous System any longer. So far, in order to avoid over-interpreted data from mathematical models and to take more consideration of clinical significance, the PBPK/PD model with Automatic Nervous System presented is helpful to make clearer relationship between PK and PD, that between sophisticated of model and real physiological phenomenon in Anesthesia than before.

Pharmacokinetic and pharmacodynamic interaction between irbesartan and hydrochlorothiazide in renal hypertensive dogs

Combined irbesartan/hydrochlorothiazide (HCTZ) formulations are often used clinically. Pharmacokinetic-pharmacodynamic (PK/PD) modeling was applied to investigate the pharmacokinetic and pharmacodynamic interaction between irbesartan and HCTZ in renal hypertensive dogs at non-steady-state and steady-state. The renal hypertensive dogs were treated with oral irbesartan alone, or HCTZ alone, or the combination of irbesartan and HCTZ for 8 days. Blood pressure and plasma concentrations were measured and pharmacokinetic-pharmacodynamic parameters were analyzed. Irbesartan showed a two-compartment model pharmacokinetic profile. The concentration-time course of irbesartan was not changed by HCTZ, but irbesartan increased the peak plasma concentration and area under the curve of HCTZ at steady-state. HCTZ had no blood pressure lowering effect at non-steady-state. Irbesartan plus HCTZ had greater blood pressure lowering action than irbesartan alone. HCTZ increased actions of irbesartan. Hysteresis loops were found between effect and plasma concentrations of irbesartan after a single dose. However, hysteresis loops disappeared at steady state with more rapid realization of maximum concentration and effects. The relationship between effects and effect-compartment concentrations of the drugs was represented by a sigmoid Emax model. The results suggest synergistic pharmacodynamic interaction between irbesartan and HCTZ in renal hypertensive dogs and some differences of pharmacokinetic-pharmacodynamic properties between irbesartan and irbesartan/HCTZ combinations at non-steady-state and steady state.

Biomarkers, validation and pharmacokinetic-pharmacodynamic modelling

Four elements are crucial to successful pharmacokinetic-pharmacodynamic (PK/PD) modelling and simulation for efficient and effective rational drug development: (i) mechanism-based biomarker selection and correlation to clinical endpoints; (ii) quantification of drug and/or metabolites in biological fluids under good laboratory practices (GLP); (iii) GLP-like biomarker method validation and measurements and; (iv) mechanism-based PK/PD modelling and validation. Biomarkers can provide great predictive value in early drug development if they reflect the mechanism of action for the intervention even if they do not become surrogate endpoints. PK/PD modelling and simulation can play a critical role in this process. Data from genomic and proteomics differentiating healthy versus disease states lead to biomarker discovery and identification. Multiple genes control complex diseases via hosts of gene products in biometabolic pathways and cell/organ signal transduction. Pilot exploratory studies should be conducted to identify pivotal biomarkers to be used for predictive clinical assessment of disease progression and the effect of drug intervention. Most biomarkers are endogenous macromolecules, which could be measured in biological fluids. Many exist in heterogeneous forms with varying activity and immunoreactivity, posting challenges for bioanalysis. Reliable and selective assays could be validated under a GLP-like environment for quantitative methods. While the need for consistent reference standards and quality control monitoring during sample analysis for biomarker assays are similar to that of drug molecules, many biomarkers have special requirements for sample collection that demand a well coordinated team management. Bioanalytical methods should be validated to meet study objectives at various drug development stages, and possess adequate performance to quantify biochemical responses specific to the target disease progression and drug intervention. Protocol design to produce sufficient data for PK/PD modelling would be more complex than that of PK. Knowledge of mechanism from discovery and preclinical studies are helpful for planning clinical study designs in cascade, sequential, crossover or replicate mode. The appropriate combination of biomarker identification and selection, bioanalytical methods development and validation for drugs and biomarkers, and mechanism-based PK/PD models for fitting data and predicting future clinical endpoints/outcomes provide powerful insights and guidance for effective and efficient rational drug development, toward safe and efficacious medicine for individual patients.