Heart rate variability: reference values and role for clinical profile and mortality in individuals with heart failure

AIMS

To establish reference values and clinically relevant determinants for measures of heart rate variability (HRV) and to assess their relevance for clinical outcome prediction in individuals with heart failure.

METHODS

Data from the MyoVasc study (NCT04064450; N = 3289), a prospective cohort on chronic heart failure with a highly standardized, 5 h examination, and Holter ECG recording were investigated. HRV markers were selected using a systematic literature screen and a data-driven approach. Reference values were determined from a healthy subsample. Clinical determinants of HRV were investigated via multivariable linear regression analyses, while their relationship with mortality was investigated by multivariable Cox regression analyses.

RESULTS

Holter ECG recordings were available for analysis in 1001 study participants (mean age 64.5 ± 10.5 years; female sex 35.4%). While the most frequently reported HRV markers in literature were from time and frequency domains, the data-driven approach revealed predominantly non-linear HRV measures. Age, sex, dyslipidemia, family history of myocardial infarction or stroke, peripheral artery disease, and heart failure were strongly related to HRV in multivariable models. In a follow-up period of 6.5 years, acceleration capacity [HR_perSD 1.53 (95% CI 1.21/1.93), p = 0.0004], deceleration capacity [HR_perSD: 0.70 (95% CI 0.55/0.88), p = 0.002], and time lag [HR_perSD 1.22 (95% CI 1.03/1.44), p = 0.018] were the strongest predictors of all-cause mortality in individuals with heart failure independently of cardiovascular risk factors, comorbidities, and medication.

CONCLUSION

HRV markers are associated with the cardiovascular clinical profile and are strong and independent predictors of survival in heart failure. This underscores clinical relevance and interventional potential for individuals with heart failure.

CLINICALTRIALS

GOV IDENTIFIER

NCT04064450.

Model‐informed bridging of rivaroxaban doses for thromboprophylaxis in pediatric patients aged 9 years and older with congenital heart disease

Rivaroxaban is approved in various regions for the treatment of acute venous thromboembolism (VTE) in children aged between 0 and 18 years and was recently investigated for thromboprophylaxis in children aged between 2 and 8 years (with body weights <30 kg) with congenital heart disease who had undergone the Fontan procedure. In the absence of clinical data, rivaroxaban doses for thromboprophylaxis in post‐Fontan children aged 9 years and older or ≥30 kg were derived by a bridging approach that used physiologically‐based pharmacokinetic (PBPK) and population pharmacokinetic (popPK) models based on pharmacokinetic (PK) data from 588 pediatric patients and from adult patients who received 10 mg once daily for thromboprophylaxis after major orthopedic surgeries as a reference. Both models showed a tendency toward underestimating rivaroxaban exposure in post‐Fontan patients aged between 2 and 5 years but accurately described rivaroxaban PK in post‐Fontan patients aged between 5 and 8 years. Under the assumption that hepatic function is not impaired in post‐Fontan patients, PBPK and popPK simulations indicated that half of the rivaroxaban doses for the same body weight given to pediatric patients treated for acute VTE would yield in pediatric post‐Fontan patients exposures similar to the exposure observed in adult patients receiving 10 mg rivaroxaban once daily for thromboprophylaxis. Simulation‐derived doses (7.5 mg rivaroxaban once daily for body weights 30–<50 kg and 10 mg once daily for body weights ≥50 kg) were therefore included in the recent US label of rivaroxaban for thromboprophylaxis in children aged 2 years and older with congenital heart disease who have undergone the Fontan procedure.

Early prediction of decompensation (EPOD) score: Non-invasive determination of cirrhosis decompensation risk

BACKGROUND & AIMS

Decompensation is a hallmark of disease progression in cirrhotic patients. Early detection of a phase transition from compensated cirrhosis to decompensation would enable targeted therapeutic interventions potentially extending life expectancy. This study aims to (a) identify the predictors of decompensation in a large, multicentric cohort of patients with compensated cirrhosis, (b) to build a reliable prognostic score for decompensation and (c) to evaluate the score in independent cohorts.

METHODS

Decompensation was identified in electronic health records data from 6049 cirrhosis patients in the IBM Explorys database training cohort by diagnostic codes for variceal bleeding, encephalopathy, ascites, hepato-renal syndrome and/or jaundice. We identified predictors of clinical decompensation and developed a prognostic score using Cox regression analysis. The score was evaluated using the IBM Explorys database validation cohort (N = 17662), the Penn Medicine BioBank (N = 1326) and the UK Biobank (N = 317).

RESULTS

The new Early Prediction of Decompensation (EPOD) score uses platelet count, albumin, and bilirubin concentration. It predicts decompensation during a 3-year follow-up in three validation cohorts with AUROCs of 0.69, 0.69 and 0.77, respectively, and outperforms the well-known MELD and Child-Pugh score in predicting decompensation. Furthermore, the EPOD score predicted the 3-year probability of decompensation.

CONCLUSIONS

The EPOD score provides a prediction tool for the risk of decompensation in patients with cirrhosis that outperforms well-known cirrhosis scores. Since EPOD is based on three blood parameters, only, it provides maximal clinical feasibility at minimal costs.

Leveraging translational approaches for accelerated clinical development of vericiguat

Background/Introduction

Vericiguat is a soluble guanylate cyclase (sGC) stimulator, like riociguat and nelociguat, and entered clinical development in 2012. Before entering Phase 2, pharmacokinetics (PK) and pharmacodynamics (PD) of vericiguat had been studied in healthy volunteers only, whereas riociguat and nelociguat had also been studied in patients with pulmonary hypertension (PH) and left ventricular dysfunction (LVD) or biventricular chronic heart failure (HF). We hypothesised that integrating all PK/PD data from these compounds into population PK/PD (popPK/PD) and physiology-based PK (PBPK) models could be used to predict optimal and safe dose ranges of vericiguat for Phase 2b studies in patients with worsening chronic HF. This novel bridging approach was applied in one of several translational stages to accelerate the development of vericiguat (Figure 1).

Purpose

We used prior knowledge from other sGC stimulators in a combined PK/PD and PBPK modelling approach to directly initiate Phase 2b studies of vericiguat in patients after Phase 1 studies in healthy volunteers.

Methods

PK, heart rate (HR) and systemic vascular resistance (SVR) data for vericiguat, nelociguat and riociguat were used to calculate PK/PD slopes of linear models, corrected with fraction unbound percentages (2.2%, 3.6% and 3.9%, respectively), to compare potency relative to riociguat based on unbound concentrations. PK estimates for nelociguat and riociguat were derived using population PK modelling (NONMEM) from patient studies with sparse PK sampling. PBPK models informed by preclinical physicochemical and PK data as well as clinical data for vericiguat were used to predict vericiguat PK in patients with HF (PK-Sim). Exposure–response data for riociguat in patients indicated the optimal range of PD responses for vericiguat (blood pressure for safety and cardiac index for efficacy).

Results

Vericiguat and nelociguat had lower potency than riociguat when comparing PK/PD slopes for HR and SVR (slope ratios of 0.23–0.32 for vericiguat and 0.33–0.47 for nelociguat). Plasma concentrations of vericiguat would need to be ∼3.6 times that of riociguat for equivalent responses. In patients with PH and LVD the optimal plasma concentration range for riociguat was ∼10–100 μg/l in exposure–response and safety studies, which translates to a target exposure range of ∼90–900 μg/l for vericiguat in patients with HF. PBPK modelling showed that vericiguat 2.5 mg and 10 mg would cover the target exposure range and that 1.25 mg would be a “non-effective” dose level with respect to haemodynamics.

Conclusions

Our novel translational approach combining popPK/PD analyses of other sGC stimulators with PBPK modelling enabled vericiguat to move directly from Phase 1 to Phase 2b, reducing development time by ∼2 years. PK and safety results from Phase 2b (SOCRATES-REDUCED) and Phase 3 (VICTORIA) trials confirmed that use of this translational approach to predict dose ranges of vericiguat was successful.

Funding Acknowledgement

Type of funding sources: Private company. Main funding source(s): Funding for this research was provided by Bayer AG, Berlin, Germany Figure 1

Population pharmacokinetic analysis of rivaroxaban in children and comparison to prospective physiologically-based pharmacokinetic predictions

Rivaroxaban has been investigated in the EINSTEIN‐Jr program for the treatment of acute venous thromboembolism (VTE) in children aged 0 to 18 years and in the UNIVERSE program for thromboprophylaxis in children aged 2 to 8 years with congenital heart disease after Fontan‐procedure. Physiologically‐based pharmacokinetic (PBPK) and population pharmacokinetic (PopPK) modeling were used throughout the pediatric development of rivaroxaban according to the learn‐and‐confirm paradigm. The development strategy was to match pediatric drug exposures to adult exposure proven to be safe and efficacious. In this analysis, a refined pediatric PopPK model for rivaroxaban based on integrated EINSTEIN‐Jr data and interim PK data from part A of the UNIVERSE phase III study was developed and the influence of potential covariates and intrinsic factors on rivaroxaban exposure was assessed. The model adequately described the observed pediatric PK data. PK parameters and exposure metrics estimated by the PopPK model were compared to the predictions from a previously published pediatric PBPK model for rivaroxaban. Ninety‐one percent of the individual post hoc clearance estimates were found within the 5th to 95th percentile of the PBPK model predictions. In patients below 2 years of age, however, clearance was underpredicted by the PBPK model. The iterative and integrative use of PBPK and PopPK modeling and simulation played a major role in the establishment of the bodyweight‐adjusted rivaroxaban dosing regimen that was ultimately confirmed to be a safe and efficacious dosing regimen for children aged 0 to 18 years with acute VTE in the EINSTEIN‐Jr phase III study.

Predictive Performance of Physiology-Based Pharmacokinetic Dose Estimates for Pediatric Trials: Evaluation With 10 Bayer Small-Molecule Compounds in Children

Development and guidance of dosing schemes in children have been supported by physiology-based pharmacokinetic (PBPK) modeling for many years. PBPK models are built on a generic basis, where compound- and system-specific parameters are separated and can be exchanged, allowing the translation of these models from adults to children by accounting for physiological differences. Owing to these features, PBPK modeling is a valuable approach to support clinical decision making for dosing in children. In this analysis, we evaluate pediatric PBPK models for 10 small-molecule compounds that were applied to support clinical decision processes at Bayer for their predictive power in different age groups. Ratios of PBPK-predicted to observed PK parameters for the evaluated drugs in different pediatric age groups were estimated. Predictive performance was analyzed on the basis of a 2-fold error range and the bioequivalence range (ie, 0.8 ≤ predicted/observed ≤ 1.25). For all 10 compounds, all predicted-to-observed PK ratios were within a 2-fold error range (n = 27), with two-thirds of the ratios within the bioequivalence range (n = 18). The findings demonstrate that the pharmacokinetics of these compounds was successfully and adequately predicted in different pediatric age groups. This illustrates the applicability of PBPK for guiding dosing schemes in the pediatric population.

Applications of Physiologically Based Pharmacokinetic Modeling of Rivaroxaban-Renal and Hepatic Impairment and Drug-Drug Interaction Potential

The non–vitamin K antagonist oral anticoagulant rivaroxaban is used in several thromboembolic disorders. Rivaroxaban is eliminated via both metabolic degradation and renal elimination as unchanged drug. Therefore, renal and hepatic impairment may reduce rivaroxaban clearance, and medications inhibiting these clearance pathways could lead to drug‐drug interactions. This physiologically based pharmacokinetic (PBPK) study investigated the pharmacokinetic behavior of rivaroxaban in clinical situations where drug clearance is impaired. A PBPK model was developed using mass balance and bioavailability data from adults and qualified using clinically observed data. Renal and hepatic impairment were simulated by adjusting disease‐specific parameters, and concomitant drug use was simulated by varying enzyme activity in virtual populations (n = 1000) and compared with pharmacokinetic predictions in virtual healthy populations and clinical observations. Rivaroxaban doses of 10 mg or 20 mg were used. Mild to moderate renal impairment had a minor effect on area under the concentration‐time curve and maximum plasma concentration of rivaroxaban, whereas severe renal impairment caused a more pronounced increase in these parameters vs normal renal function. Area under the concentration‐time curve and maximum plasma concentration increased with severity of hepatic impairment. These effects were smaller in the simulations compared with clinical observations. AUC and C_max increased with the strength of cytochrome P450 3A4 and P‐glycoprotein inhibitors in simulations and clinical observations. This PBPK model can be useful for estimating the effects of impaired drug clearance on rivaroxaban pharmacokinetics. Identifying other factors that affect the pharmacokinetics of rivaroxaban could facilitate the development of models that approximate real‐world pharmacokinetics more accurately.

A Physiologically-Based Pharmacokinetic Model to Describe Ciprofloxacin Pharmacokinetics Over the Entire Span of Life

Background

Physiologically-based pharmacokinetic (PBPK) modeling has received growing interest as a useful tool for the assessment of drug pharmacokinetics by continuous knowledge integration.

Objective

The objective of this study was to build a ciprofloxacin PBPK model for intravenous and oral dosing based on a comprehensive literature review, and evaluate the predictive performance towards pediatric and geriatric patients.

Methods

The aim of this report was to establish confidence in simulations of the ciprofloxacin PBPK model along the development process to facilitate reliable predictions outside of the tested adult age range towards the extremes of ages. Therefore, mean data of 69 published clinical trials were identified and integrated into the model building, simulation and verification process. The predictive performance on both ends of the age scale was assessed using individual data of 258 subjects observed in own clinical trials.

Results

Ciprofloxacin model verification demonstrated no concentration-related bias and accurate simulations for the adult age range, with only 4.8% of the mean observed data points for intravenous administration and 12.1% for oral administration being outside the simulated twofold range. Predictions towards the extremes of ages for the area under the plasma concentration–time curve (AUC) and the maximum plasma concentration (C_max) over the entire span of life revealed a reliable estimation, with only two pediatric AUC observations outside the 90% prediction interval.

Conclusion

Overall, this ciprofloxacin PBPK modeling approach demonstrated the predictive power of a thoroughly informed middle-out approach towards age groups of interest to potentially support the decision-making process.

Electronic supplementary material

The online version of this article (10.1007/s40262-018-0661-6) contains supplementary material, which is available to authorized users.

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.

Application of Physiologically-Based and Population Pharmacokinetic Modeling for Dose Finding and Confirmation During the Pediatric Development of Moxifloxacin

Moxifloxacin is a widely used fluoroquinolone for the treatment of complicated intra‐abdominal infections. We applied physiologically‐based pharmacokinetic (PBPK) and population pharmacokinetic (popPK) modeling to support dose selection in pediatric patients. We scaled an existing adult PBPK model to children based on prior physiological knowledge. The resulting model proposed an age‐dependent dosing regimen that was tested in a phase I study. Refined doses were then tested in a phase III study. A popPK analysis of all clinical pediatric data confirmed the PBPK predictions, including the proposed dosing schedule in children, and supported pharmacokinetics‐related safety/efficacy questions. The pediatric PBPK model adequately predicted the doses necessary to achieve antimicrobial efficacy while maintaining safety in the phase I and III pediatric studies. Altogether, this study retroactively demonstrated the robustness and utility of modeling to support dose finding and confirmation in pediatric drug development for moxifloxacin.

Pharmacokinetics of rivaroxaban in children using physiologically based and population pharmacokinetic modelling: an EINSTEIN-Jr phase I study

BACKGROUND

The EINSTEIN-Jr program will evaluate rivaroxaban for the treatment of venous thromboembolism (VTE) in children, targeting exposures similar to the 20 mg once-daily dose for adults. A physiologically based pharmacokinetic (PBPK) model for pediatric rivaroxaban dosing has been constructed.

METHODS

We quantitatively assessed the pharmacokinetics (PK) of a single rivaroxaban dose in children using population pharmacokinetic (PopPK) modelling and assessed the applicability of the PBPK model. Plasma concentration-time data from the EINSTEIN-Jr phase I study were analysed by non-compartmental and PopPK analyses and compared with the predictions of the PBPK model. Two rivaroxaban dose levels, equivalent to adult doses of rivaroxaban 10 mg and 20 mg, and two different formulations (tablet and oral suspension) were tested in children aged 0.5-18 years who had completed treatment for VTE.

RESULTS

PK data from 59 children were obtained. The observed plasma concentration-time profiles in all subjects were mostly within the 90% prediction interval, irrespective of dose or formulation. The PopPK estimates and non-compartmental analysis-derived PK parameters (in children aged ≥6 years) were in good agreement with the PBPK model predictions.

CONCLUSIONS

These results confirmed the applicability of the rivaroxaban pediatric PBPK model in the pediatric population aged 0.5-18 years, which in combination with the PopPK model, will be further used to guide dose selection for the treatment of VTE with rivaroxaban in EINSTEIN-Jr phase II and III studies.

TRIAL REGISTRATION

ClinicalTrials.gov number, NCT01145859; registration date: 17 June 2010.

Development of a Physiologically-Based Pharmacokinetic Model for Preterm Neonates: Evaluation with In Vivo Data

Among pediatric patients, preterm neonates and newborns are the most vulnerable subpopulation. Rapid developmental changes of physiological factors affecting the pharmacokinetics of drug substances in newborns require extreme care in dose and dose regimen decisions. These decisions could be supported by in silico methods such as physiologically-based pharmacokinetic (PBPK) modeling. In a comprehensive literature search, the physiological information of preterm neonates that is required to establish a PBPK model has been summarized and implemented into the database of a generic PBPK software. Physiological parameters include the organ weights and blood flow rates, tissue composition, as well as ontogeny information about metabolic and elimination processes in the liver and kidney. The aim of this work is to evaluate the model's accuracy in predicting the pharmacokinetics following intravenous administration of two model drugs with distinct physicochemical properties and elimination pathways based on earlier reported in vivo data. To this end, PBPK models of amikacin and paracetamol have been set up to predict their plasma levels in preterm neonates. Predicted plasma concentration-time profiles were compared to experimentally obtained in vivo data. For both drugs, plasma concentration time profiles following single and multiple dosing were appropriately predicted for a large range gestational and postnatal ages. In summary, PBPK simulations in preterm neonates appear feasible and might become a useful tool in the future to support dosing decisions in this special patient population.

Evaluation of changes in oral drug absorption in preterm and term neonates for Biopharmaceutics Classification System (BCS) class I and II compounds

AIMS

Evidence suggests that the rate of oral drug absorption changes during early childhood. Yet, respective clinical implications are currently unclear, particularly for preterm neonates. The objective of this study was to evaluate changes in oral drug absorption after birth for different Biopharmaceutics Classification System (BCS) class I and II compounds to better understand respective implications for paediatric pharmacotherapy.

METHODS

Two paradigm compounds were selected for BCS class I (paracetamol (acetaminophen) and theophylline) and II (indomethacin and ibuprofen), respectively, based on the availability of clinical literature data following intravenous and oral dosing. A comparative population pharmacokinetic analysis was performed in a step-wise manner in NONMEM® 7.2 to characterize and predict changes in oral drug absorption after birth for paracetamol, theophylline and indomethacin.

RESULTS

A one compartment model with an age-dependent maturation function for oral drug absorption was found appropriate to characterize the pharmacokinetics of paracetamol. Our findings indicate that the rate at which a drug is absorbed from the GI tract reaches adult levels within about 1 week after birth. The maturation function for paracetamol was found applicable to theophylline and indomethacin once solubility limitations were overcome via drug formulation. The influence of excipients on solubility and, hence, oral bioavailability was confirmed for ibuprofen, a second BCS class II compound.

CONCLUSIONS

The findings of our study suggest that the processes underlying changes in oral drug absorption after birth are drug-independent and that the maturation function identified for paracetamol may be generally applicable to other BCS class I and II compounds for characterizing drug absorption in preterm as well as term neonates.

Contribution of rivaroxaban to the international normalized ratio when switching to warfarin for anticoagulation as determined by simulation studies

AIM

This study evaluated the influence of rivaroxaban 20 mg once daily on international normalized ratio (INR) during the co-administration period when switching from rivaroxaban to warfarin.

METHODS

We developed a calibrated coagulation model that was qualified with phase I clinical data. Prothrombin time and INR values were simulated by use of phospholipid concentrations that matched Neoplastin Plus® and Innovin® reagents. To simulate the combined effects of rivaroxaban and warfarin on INR during switching, warfarin initiation was simulated by adjusting the magnitude of the warfarin effect to reach the desired target INRs over the course of 21 days. The warfarin effect values (obtained every 6 h) and the desired rivaroxaban plasma concentrations were used. Nomograms were generated from rivaroxaban induced increases in INR.

RESULTS

The simulation had good prediction quality. Rivaroxaban induced increases in the total INR from the warfarin attributed INR were seen, which increased with rivaroxaban plasma concentration. When the warfarin only INR was 2.0-3.0, the INR contribution of rivaroxaban with Neoplastin Plus® was 0.5-1.2, decreasing to 0.3-0.6 with Innovin® at median trough rivaroxaban plasma concentrations (38 μg l(-1) ).

CONCLUSIONS

The data indicate that measuring warfarin induced changes in INR are best performed at trough rivaroxaban concentrations (24 h after rivaroxaban dosing) during the co-administration period when switching from rivaroxaban to warfarin. Furthermore, Innovin® is preferable to Neoplastin Plus® because of its substantially lower sensitivity to rivaroxaban, thereby reducing the influence of rivaroxaban on the measured INR.

Computational investigation of potential dosing schedules for a switch of medication from warfarin to rivaroxaban-an oral, direct Factor Xa inhibitor

The long-lasting anticoagulant effect of vitamin K antagonists can be problematic in cases of adverse drug reactions or when patients are switched to another anticoagulant therapy. The objective of this study was to examine in silico the anticoagulant effect of rivaroxaban, an oral, direct Factor Xa inhibitor, combined with the residual effect of discontinued warfarin. Our simulations were based on the recommended anticoagulant dosing regimen for stroke prevention in patients with atrial fibrillation. The effects of the combination of discontinued warfarin plus rivaroxaban were simulated using an extended version of a previously validated blood coagulation computer model. A strong synergistic effect of the two distinct mechanisms of action was observed in the first 2–3 days after warfarin discontinuation; thereafter, the effect was close to additive. Nomograms for the introduction of rivaroxaban therapy after warfarin discontinuation were derived for Caucasian and Japanese patients using safety and efficacy criteria described previously, together with the coagulation model. The findings of our study provide a mechanistic pharmacologic rationale for dosing schedules during the therapy switch from warfarin to rivaroxaban and support the switching strategies as outlined in the Summary of Product Characteristics and Prescribing Information for rivaroxaban.

Evolution of a detailed physiological model to simulate the gastrointestinal transit and absorption process in humans, part II: extension to describe performance of solid dosage forms

The physiological absorption model presented in part I of this work is now extended to account for dosage-form-dependent gastrointestinal (GI) transit as well as disintegration and dissolution processes of various immediate-release and modified-release dosage forms. Empirical functions of the Weibull type were fitted to experimental in vitro dissolution profiles of solid dosage forms for eight test compounds (aciclovir, caffeine, cimetidine, diclofenac, furosemide, paracetamol, phenobarbital, and theophylline). The Weibull functions were then implemented into the model to predict mean plasma concentration-time profiles of the various dosage forms. On the basis of these dissolution functions, pharmacokinetics (PK) of six model drugs was predicted well. In the case of diclofenac, deviations between predicted and observed plasma concentrations were attributable to the large variability in gastric emptying time of the enteric-coated tablets. Likewise, oral PK of furosemide was found to be predominantly governed by the gastric emptying patterns. It is concluded that the revised model for GI transit and absorption was successfully integrated with dissolution functions of the Weibull type, enabling prediction of in vivo PK profiles from in vitro dissolution data. It facilitates a comparative analysis of the parameters contributing to oral drug absorption and is thus a powerful tool for formulation design.

Evaluation of the efficacy and safety of rivaroxaban using a computer model for blood coagulation

Rivaroxaban is an oral, direct Factor Xa inhibitor approved in the European Union and several other countries for the prevention of venous thromboembolism in adult patients undergoing elective hip or knee replacement surgery and is in advanced clinical development for the treatment of thromboembolic disorders. Its mechanism of action is antithrombin independent and differs from that of other anticoagulants, such as warfarin (a vitamin K antagonist), enoxaparin (an indirect thrombin/Factor Xa inhibitor) and dabigatran (a direct thrombin inhibitor). A blood coagulation computer model has been developed, based on several published models and preclinical and clinical data. Unlike previous models, the current model takes into account both the intrinsic and extrinsic pathways of the coagulation cascade, and possesses some unique features, including a blood flow component and a portfolio of drug action mechanisms. This study aimed to use the model to compare the mechanism of action of rivaroxaban with that of warfarin, and to evaluate the efficacy and safety of different rivaroxaban doses with other anticoagulants included in the model. Rather than reproducing known standard clinical measurements, such as the prothrombin time and activated partial thromboplastin time clotting tests, the anticoagulant benchmarking was based on a simulation of physiologically plausible clotting scenarios. Compared with warfarin, rivaroxaban showed a favourable sensitivity for tissue factor concentration inducing clotting, and a steep concentration–effect relationship, rapidly flattening towards higher inhibitor concentrations, both suggesting a broad therapeutic window. The predicted dosing window is highly accordant with the final dose recommendation based upon extensive clinical studies.

A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks

Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim^® and MoBi^® capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug–drug, or drug–metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach.