Khaled Abduljalil

Anatomical, Physiological and Metabolic Changes with Gestational Age during Normal Pregnancy A Database for Parameters Required in Physiologically Based Pharmacokinetic Modelling

AbstractBackground: Pregnancy is associated with considerable changes in the physiological, anatomical and biochemical attributes in women. These may alter the exposure to xenobiotics between pregnant and non-pregnant women who receive similar doses, with implications for different susceptibility to environmental pollutants or therapeutic agents. Physiologically based pharmacokinetic (PBPK) models together with in vitro in vivo extrapolation (IVIVE) of absorption, distribution, metabolism and excretion (ADME) characteristics may capture the likely changes. However, such models require comprehensive information on the longitudinal variations of PBPK parameter values; a set of data that are as yet not available from a singular source. Aim: The aim of this article was to collect, integrate and analyse the available time-variant parameters that are needed for the PBPK modelling of xenobiotic kinetics in a healthy pregnant population. Methods: A structured literature search was carried out on anatomical, physiological and biochemical parameters likely to change in pregnancy and alter the kinetics of xenobiotics. Collated data were carefully assessed, integrated and analysed for trends with gestational age. Algorithms were generated to describe the changes in parameter values with gestational age. These included changes in maternal weight, the individual organ volumes and blood flows, glomerular filtration rates, and some drug-metabolising enzyme activities. Results: Articles were identified using relevant keywords, quality appraised and data were extracted by two investigators. Some parameters showed no change with gestational age and for others robust data were not available. However, for many parameters significant changes were reported during the course of pregnancy, e.g. cardiac output, protein binding and expression/activity of metabolizing enzymes. The trend for time-variant parameters was not consistent (with respect to direction and mono-tonicity). Hence, various mathematical algorithms were needed to describe individual parameter values. Conclusion: Despite the limitations identified in the availability of some values, the collected data presented in this paper provide a potentially useful singular resource for key parameters needed for PBPK modelling in pregnancy. This facilitates the risk assessment of environmental chemicals and therapeutic drug dose adjustments in the pregnant population.

A pregnancy physiologically based pharmacokinetic (p‐PBPK) model for disposition of drugs metabolized by CYP1A2, CYP2D6 and CYP3A4

AIMS Pregnant women are usually not part of the traditional drug development programme. Pregnancy is associated with major biological and physiological changes that alter the pharmacokinetics (PK) of drugs. Prediction of the changes to drug exposure in this group of patients may help to prevent under- or overtreatment. We have used a pregnancy physiologically based pharmacokinetic (p-PBPK) model to assess the likely impact of pregnancy on three model compounds, namely caffeine, metoprolol and midazolam, based on the knowledge of their disposition in nonpregnant women and information from in vitro studies. METHODS A perfusion-limited form of a 13-compartment full-PBPK model (Simcyp® Simulator) was used for the nonpregnant women, and this was extended to the pregnant state by applying known changes to all model components (including the gestational related activity of specific cytochrome P450 enzymes) and through the addition of an extra compartment to represent the fetoplacental unit. The uterus and the mammary glands were grouped into the muscle compartment. The model was implemented in Matlab Simulink and validated using clinical observations. RESULTS The p-PBPK model predicted the PK changes of three model compounds (namely caffeine, metoprolol and midazolam) for CYP1A2, CYP2D6 and CYP3A4 during pregnancy within twofold of observed values. The changes during the third trimester were predicted to be a 100% increase, a 30% decrease and a 35% decrease in the exposure of caffeine, metoprolol and midazolam, respectively, compared with the nonpregnant women. CONCLUSIONS In the absence of clinical data, the in silico prediction of PK behaviour during pregnancy can provide a valuable aid to dose adjustment in pregnant women. The performance of the model for drugs metabolized by a single enzyme to different degrees (high and low extraction) and for drugs that are eliminated by several different routes warrants further study.

Does age affect gastric emptying time? A model‐based meta‐analysis of data from premature neonates through to adults

Purpose. Gastric emptying (GE) is often reported to be slower and more irregular in premature neonates than in older children and adults. The aim of this study was to investigate the impact of age and other covariates on the rate of GE. Methods. The effect of age on the mean gastric residence times (MGRT) of liquid and solid food was assessed by analysing 49 published studies of 1457 individuals, aged from 28 weeks gestation to adults. The data were modelled using the nonlinear mixed‐effects approach within NONMEM version 7.2 (ICON, Dublin, Ireland), with evaluation of postnatal age, gestational age and meal type as covariates. A double Weibull function was selected as a suitable model since it could account for the typical biphasic nature of GE. Results. Age was not a significant covariate for GE but meal type was. Aqueous solutions were associated with the fastest emptying time (mean simulated gastric residence time of 45 min) and solid food was associated with the slowest (98 min). Conclusions. These findings challenge the assertion that GE is different in neonates, as compared with older children and adults due to age, and they reinforce the significance of food type in modulating GE.

Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability

This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

Physiologically-based Pharmacokinetic (PBPK) Models for Assessing the Kinetics of Xenobiotics during Pregnancy: Achievements and Shortcomings

The physiological changes that occur in the maternal body and the placental-foetal unit during pregnancy influence the absorption, distribution, metabolism, and excretion (ADME) of xenobiotics. These include drugs that are prescribed for therapeutic reasons or chemicals to which women are exposed unintentionally from the surrounding environment. The pregnancy physiologically-based pharmacokinetic (p-PBPK) models developed for theoretical assessment of the kinetics of xenobiotics during pregnancy should take into account all the dynamic changes of the maternal and embryonic/foetal physiological functions. A number of p-PBPK models have been reported for pregnant animals and humans in the past 3 decades which have mainly been applied in the risk assessment of various environmental chemicals. The purpose of this review is to critically evaluate the current state of the art in p-PBPK modelling and to recommend potential steps that could be taken to improve model development and its application particularly in drug discovery and development for pregnant women, with potential implications for optimal drug treatment in pregnancy. The pregnancy-induced changes in physiology and pharmacokinetics, including metabolism, are reviewed to illustrate the basic alterations essential for pregnancy model development. A systemic search of the literature for existing p-PBPK models is carried out and the model structures, governing equations, methods of modelling growth, model validation/verification as well as model applications are highlighted. This review discusses benefits and limitations of the reported p-PBPK models so far and suggests areas for model improvement. The need for establishing databases on the system-related (biological, anatomical and physiological) and drug-related (physiochemical, affinity to enzymes and transpoorters) parameters for healthy and unhealthy pregnancies is particularly emphasized.

Physiologically-Based Pharmacokinetics

External control of tissues and cells, by hormones, nerves, and other stimuli, involves the transduction of signals from ligand-activated receptors to control of rate-limiting enzymes or proteins that affect key steps in metabolism, gene transcription or other processes within the cells. The signal transduction is carried out by a network of interacting signal mediators, i.e. proteins and small molecule transducers. Such signaling transduction networks display a high degree of complexity, which is due to the presence of feed-forward and feedback loops, both negative and positive, and to the fact that interactions change over time and according to intracellular location. In combination with multiple layers of control, redundancy, shared signal mediators, shared signal paths, and cross-talk between signals, this leads to a complexity that poses new challenges to progress in dissecting and understanding cellular control. Furthermore, many diseases, such as cancer, insulin resistance, and type 2 diabetes, are associated with malfunctioning in the complex signaling networks.

More Power to OATP1B1: An Evaluation of Sample Size in Pharmacogenetic Studies Using a Rosuvastatin PBPK Model for Intestinal, Hepatic, and Renal Transporter‐Mediated Clearances

Rosuvastatin is a substrate of choice in clinical studies of organic anion‐transporting polypeptide (OATP)1B1‐ and OATP1B3‐associated drug interactions; thus, understanding the effect of OATP1B1 polymorphisms on the pharmacokinetics of rosuvastatin is crucial. Here, physiologically based pharmacokinetic (PBPK) modeling was coupled with a power calculation algorithm to evaluate the influence of sample size on the ability to detect an effect (80% power) of OATP1B1 phenotype on pharmacokinetics of rosuvastatin. Intestinal, hepatic, and renal transporters were mechanistically incorporated into a rosuvastatin PBPK model using permeability‐limited models for intestine, liver, and kidney, respectively, nested within a full PBPK model. Simulated plasma rosuvastatin concentrations in healthy volunteers were in agreement with previously reported clinical data. Power calculations were used to determine the influence of sample size on study power while accounting for OATP1B1 haplotype frequency and abundance in addition to its correlation with OATP1B3 abundance. It was determined that 10 poor‐transporter and 45 intermediate‐transporter individuals are required to achieve 80% power to discriminate the AUC0‐48h of rosuvastatin from that of the extensive‐transporter phenotype. This number was reduced to 7 poor‐transporter and 40 intermediate‐transporter individuals when the reported correlation between OATP1B1 and 1B3 abundance was taken into account. The current study represents the first example in which PBPK modeling in conjunction with power analysis has been used to investigate sample size in clinical studies of OATP1B1 polymorphisms. This approach highlights the influence of interindividual variability and correlation of transporter abundance on study power and should allow more informed decision making in pharmacogenomic study design.

Considering Age Variation When Coining Drugs as High versus Low Hepatic Extraction Ratio

The hepatic extraction ratio (EH) is commonly considered an “inherent attribute” of drug. It determines the main physiological and biological elements of the system (patient attributes) that are most significant in interindividual variability of clearance. The EH consists of three age-dependent parameters: fraction of unbound drug in blood (fu.B), hepatic intrinsic clearance of unbound drug (CLu.int,H), and hepatic blood flow (QH). When the age-effects on these elements are not proportional, a given drug may shift from so-called high extraction status to low extraction. To demonstrate the impact of age-related changes on fu.B, CLu.int,H, and QH, the EH of midazolam and two hypothetical drugs with 10-fold higher and 10-fold lower CLu.int,H than midazolam were investigated in pediatrics based on known ontogeny functions. The EH was simulated using Simcyp software, version 14. This was then complemented by a comprehensive literature survey to identify the commonly applied covariates in pediatric population pharmacokinetic (PopPK) studies. Midazolam EH decreased from 0.6 in adults to 0.02 at birth, making its clearance much more susceptible to changes in CLu.int,H and fu.B than in adults and reducing the impact of QH on clearance. The drug with 10-fold higher CLu.int,H was categorized as high extraction from 4 days old onward whereas the drug with 10-fold lower CLu.int,H remained low extraction from birth to adulthood. Approximately 50% of collected PopPK studies (n = 120) did not consider interaction between age and other covariates. Interaction between covariates and age should be considered as part of studies involving younger pediatric patients. The EH cannot be considered an inherent drug property without considering the effect of age.

A Tutorial on Pharmacodynamic Scripting Facility in Simcyp

The Simcyp Simulator provides a framework for mechanistic Physiologically‐Based Pharmacokinetic/Pharmacodynamic modeling of potentially interacting drugs. It also provides a scripting facility, using the Lua language, for developing customized pharmacodynamic and toxicity models driven by drug concentrations at the site of action. We present an overview of the scripting facility including the scripting language, the editor, and how scripts are embedded within the Simulator. Examples incorporating differential equations and including inter‐individual variability on parameters are presented.

Fetal Physiologically-Based Pharmacokinetic Models: Systems Information on Fetal Biometry and Gross Composition

BackgroundPostulating fetal exposure to xenobiotics has been based on animal studies; however, inter-species differences can make this problematic. Physiologically-based pharmacokinetic models may capture the rapid changes in anatomical, biochemical, and physiological parameters during fetal growth over the duration of pregnancy and help with interpreting laboratory animal data. However, these models require robust information on the longitudinal variations of system parameter values and their covariates.ObjectiveThe objective of this study was to present an extensive analysis and integration of the available biometric data required for creating a virtual human fetal population by means of equations that define the changes of each parameter with gestational age.MethodsA comprehensive literature search was carried out on the parameters defining the growth of a fetus during in-utero life including weight, height, and body surface area in addition to other indices of fetal size, body fat, and water. Collated data were assessed and integrated through a meta-analysis to develop mathematical algorithms to describe growth with fetal age.ResultsData for the meta-analysis were obtained from 97 publications, of these, 15 were related to fetal height or length, 32 to fetal weight, 4 to fetal body surface area, 8 to crown length, 5 to abdominal circumference, 12 to head circumference, 14 to body fat, and 12 to body water. Various mathematical algorithms were needed to describe parameter values from the time of conception to birth.ConclusionThe collated data presented in this article enabled the development of mathematical functions to describe fetal biometry and provide a potentially useful resource for building anthropometric features of fetal physiologically-based pharmacokinetic models.