Personalized Drug Dosage - Closing the Loop

Subject-Specific Dosage Estimation for Primary Hypothyroidism Using Sparse Data

Subject-specific dosage estimation for primary hypothyroidism using subject-specific parameters of the thyrotropic regulation system is presented in this work. The data needed for such personalized modeling are usually sparse. This is addressed by utilizing available data along with domain knowledge for estimation of model parameters but with some uncertainty. Optimization-based dosage estimation approaches may not be applicable in the presence of such uncertainty. In this work, the optimal drug dosage range based on estimated parameter ranges for primary hypothyroid condition is estimated using the mathematical model through satisfiability modulo theory (SMT)-based analysis. The salient features of this work are as follows: (1) estimation of subject-specific model parameters with uncertainty using subject-specific pre-treatment and post-treatment observations, (2) modeling periodic drug administration as part of the ordinary differential equation model of thyrotropic regulation pathway through Fourier series approximation, (3) application of SMT-based analysis for determining optimal dosage range using this model and estimated parameter ranges, and (4) an initial dosage estimation method using the regression model. Results have been obtained to support the working of the developed computational procedures.

The potential of three-dimensional printing for pediatric oral solid dosage forms

Pediatric patients often require individualized dosing of medicine due to their unique pharmacokinetic and developmental characteristics. Current methods for tailoring the dose of pediatric medications, such as tablet splitting or compounding liquid formulations, have limitations in terms of dosing accuracy and palatability. This paper explores the potential of 3D printing as a solution to address the challenges and provide tailored doses of medication for each pediatric patient. The technological overview of 3D printing is discussed, highlighting various 3D printing technologies and their suitability for pharmaceutical applications. Several individualization options with the potential to improve adherence are discussed, such as individualized dosage, custom release kinetics, tablet shape, and palatability. To integrate the preparation of 3D printed medication at the point of care, a decentralized manufacturing model is proposed. In this setup, pharmaceutical companies would routinely provide materials and instructions for 3D printing, while specialized compounding centers or hospital pharmacies perform the printing of medication. In addition, clinical opportunities of 3D printing for dose-finding trials are emphasized. On the other hand, current challenges in adequate dosing, regulatory compliance, adherence to quality standards, and maintenance of intellectual property need to be addressed for 3D printing to close the gap in personalized oral medication.

Calibration-Free, Seconds-Resolved In Vivo Molecular Measurements using Fourier-Transform Impedance Spectroscopy Interrogation of Electrochemical Aptamer Sensors

Electrochemical aptamer-based (EAB) sensors are capable of measuring the concentrations of specific molecules in vivo, in real time, and with a few-second time resolution. For their signal transduction mechanism, these sensors utilize a binding-induced conformational change in their target-recognizing, redox-reporter-modified aptamer to alter the rate of electron transfer between the reporter and the supporting electrode. While a variety of voltammetric techniques have been used to monitor this change in kinetics, they suffer from various drawbacks, including time resolution limited to several seconds and sensor-to-sensor variation that requires calibration to remove. Here, however, we show that the use of fast Fourier transform electrochemical impedance spectroscopy (FFT-EIS) to interrogate EAB sensors leads to improved (here better than 2 s) time resolution and calibration-free operation, even when such sensors are deployed in vivo. To showcase these benefits, we demonstrate the approach's ability to perform real-time molecular measurements in the veins of living rats.

Therapeutic Drug Monitoring of Ivacaftor, Lumacaftor, Tezacaftor, and Elexacaftor in Cystic Fibrosis: Where Are We Now?

Drugs modulating the cystic fibrosis transmembrane conductance regulator (CFTR) protein, namely ivacaftor, lumacaftor, tezacaftor, and elexacaftor, are currently revolutionizing the management of patients with cystic fibrosis (CF), particularly those with at least one F508del variant (up to 85% of patients). These “caftor” drugs are mainly metabolized by cytochromes P450 3A, whose enzymatic activity is influenced by environmental factors, and are sensitive to inhibition and induction. Hence, CFTR modulators are characterized by an important interindividual pharmacokinetic variability and are also prone to drug–drug interactions. However, these CFTR modulators are given at standardized dosages, while they meet all criteria for a formal therapeutic drug monitoring (TDM) program that should be considered in cases of clinical toxicity, less-than-expected clinical response, drug or food interactions, distinct patient subgroups (i.e., pediatrics), and for monitoring short-term adherence. While the information on CFTR drug exposure–clinical response relationships is still limited, we review the current evidence of the potential interest in the TDM of caftor drugs in real-life settings.

Evaluation of Pharmacogenomics Testing of Cytochrome P450 Enzymes in the Military Health System From 2015 to 2020

Pharmacogenomics (PGx) plays a fundamental role in personalized medicine, providing an evidence-based treatment approach centered on the relationship between genomic variations and their effect on drug metabolism. Cytochrome P450 (CYP450) enzymes are responsible for the metabolism of most clinically prescribed drugs and a major source of variability in drug pharmacokinetics and pharmacodynamics. To assess the prevalence of PGx testing within the Military Health System (MHS), testing of specific CYP450 enzymes was evaluated. Data were retrospectively obtained from the Military Health System Management Analysis and Reporting Tool (M2) database. Patient demographics were identified for each test, along with TRICARE status, military treatment facility, clinic, and National Provider Identifier. A total of 929 patients received 1,833 PGx tests, predominantly composed of active duty/guard service members (N = 460; 49.5%), with highest testing rates in the army (51.5%). An even distribution in testing was observed among gender, with the highest rates in Caucasians (41.7%). Of the CYP enzymes assessed, CYP2C19 and CYP2D6 accounted for 87.8% of all PGx CYP testing. The majority of patients were tested in psychiatry clinics (N = 496; 53.4%) and primary care clinics (N = 233; 25.1%), accounting for 56.4% and 24.8% of all tests, respectively. Testing was found to be provider driven, suggesting a lack of a standardized approach to PGx and its application in patient care within the MHS. We initially recommend targeted education and revising testing labels to be more uniform and informative. Long-term recommendations include establishing pharmacy-driven protocols and point-of-care PGx testing to optimize patient outcomes.

Surviving Sepsis Campaign: Research Opportunities for Infection and Blood Purification Therapies

Patients with infection can develop sepsis, and their mortality can be high. An important aspect in the treatment of sepsis is adequate management of the infection.

Physiologically Based Pharmacokinetic Models Are Effective Support for Pediatric Drug Development

Pediatric drug development faces many difficulties. Traditionally, pediatric drug doses are simply calculated linearly based on the body weight, age, and body surface area of adults. Due to the ontogeny of children, this simple linear scaling may lead to drug overdose in pediatric patients. The physiologically based pharmacokinetic (PBPK) model, as a mathematical model, contributes to the research and development of pediatric drugs. An example of a PBPK model guiding drug dose selection in pediatrics has emerged and has been approved by the relevant regulatory agencies. In this review, we discuss the principle of the PBPK model, emphasize the necessity of establishing a pediatric PBPK model, introduce the absorption, distribution, metabolism, and excretion of the pediatric PBPK model, and understand the various applications and related prospects of the pediatric PBPK model.

Data-driven personalization of a physiologically based pharmacokinetic model for caffeine: A systematic assessment

Physiologically based pharmacokinetic (PBPK) models have been proposed as a tool for more accurate individual pharmacokinetic (PK) predictions and model‐informed precision dosing, but their application in clinical practice is still rare. This study systematically assesses the benefit of using individual patient information to improve PK predictions. A PBPK model of caffeine was stepwise personalized by using individual data on (1) demography, (2) physiology, and (3) cytochrome P450 (CYP) 1A2 phenotype of 48 healthy volunteers participating in a single‐dose clinical study. Model performance was benchmarked against a caffeine base model simulated with parameters of an average individual. In the first step, virtual twins were generated based on the study subjects' demography (height, weight, age, sex), which implicated the rescaling of average organ volumes and blood flows. The accuracy of PK simulations improved compared with the base model. The percentage of predictions within 0.8‐fold to 1.25‐fold of the observed values increased from 45.8% (base model) to 57.8% (Step 1). However, setting physiological parameters (liver blood flow determined by magnetic resonance imaging, glomerular filtration rate, hematocrit) to measured values in the second step did not further improve the simulation result (59.1% in the 1.25‐fold range). In the third step, virtual twins matching individual demography, physiology, and CYP1A2 activity considerably improved the simulation results. The percentage of data within the 1.25‐fold range was 66.15%. This case study shows that individual PK profiles can be predicted more accurately by considering individual attributes and that personalized PBPK models could be a valuable tool for model‐informed precision dosing approaches in the future.

A Critique of Pharmacokinetic Calculators for Drug Dosing Individualization

The 'one-dose-fits-all' approach where drug dosing regimen is prescribed according to recommendations from a summary of product characteristics is not appropriate for many patients whose clinical characteristics significantly differ from the most frequent ones in a population, as it cannot guarantee optimal exposure of target tissues to the drug. Our aim here is to provide a concise review of pharmacokinetic calculators currently available for clinical use and, at the same time, to suggest the minimum standards that they should satisfy to be routinely used in clinical practice. A systematic search of Medline, Ebsco, Scopus, Scindeks, Cochrane Library and Google Scholar was performed to find publications about available pharmacokinetic calculators for drug dose individualization. Theoretically well-founded and mathematically correct calculators for many drugs are available, but only a few calculators for specific drugs have been validated in clinical practice or through clinical trials, and the results published in peer-reviewed journals. The majority of available pharmacokinetic calculators for drug dosing individualization remain unvalidated, i.e., there is no evidence of their efficacy and safety in real-life clinical settings. Pharmacokinetic calculators for drug dose individualization are irreplaceable tools for achieving precision medicine, where dosing regimens are tailored to the needs and personal characteristics of each patient, maximizing efficacy and minimizing toxicity.

Design and Analysis of a Sample-and-Hold CMOS Electrochemical Sensor for Aptamer-based Therapeutic Drug Monitoring

In this paper, we present the design and the analysis of an electrochemical circuit for measuring the concentrations of therapeutic drugs using structure-switching aptamers. Aptamers are single-stranded nucleic acids, whose sequence is selected to exhibit high affinity and specificity toward a molecular target, and change its conformation upon binding. This property, when coupled with a redox reporter and electrochemical detection, enables reagent-free biosensing with a sub-minute temporal resolution for in vivo therapeutic drug monitoring. Specifically, we design a chronoamperometry-based electrochemical circuit that measures the direct changes in the electron transfer (ET) kinetics of a methylene blue reporter conjugated at the distal-end of the aptamer. To overcome the high-frequency noise amplification issue when interfacing with a large-size (> 0.25 mm2) implantable electrode, we present a sample-and-hold (S/H) circuit technique in which the desired electrode potentials are held onto noiseless capacitors during the recording of the redox currents. This allows disconnecting the feedback amplifiers to avoid its noise injection while reducing the total power consumption. A prototype circuit implemented in 65-nm CMOS demonstrates a cell-capacitance-insensitive input-referred noise (IRN) current of 15.2 pArms at a 2.5-kHz filtering bandwidth. We tested our system in human whole blood samples and measured the changes in the ET kinetics from the redox-labeled aptamers at different kanamycin concentrations. By employing principal component analysis (PCA) to compensate for the sampling errors, we report a molecular noise floor (at SNR = 1) of 3.1 µM with sub 1-sec acquisition time at 0.22-mW power consumption.

Wearable Electrochemical Sensors for the Monitoring and Screening of Drugs

Wearable electrochemical sensors capable of noninvasive monitoring of chemical markers represent a rapidly emerging digital-health technology. Recent advances toward wearable continuous glucose monitoring (CGM) systems have ignited tremendous interest in expanding such sensor technology to other important fields. This article reviews for the first time wearable electrochemical sensors for monitoring therapeutic drugs and drugs of abuse. This rapidly emerging class of drug-sensing wearable devices addresses the growing demand for personalized medicine, toward improved therapeutic outcomes while minimizing the side effects of drugs and the related medical expenses. Continuous, noninvasive monitoring of therapeutic drugs within bodily fluids empowers clinicians and patients to correlate the pharmacokinetic properties with optimal outcomes by realizing patient-specific dose regulation and tracking dynamic changes in pharmacokinetics behavior while assuring the medication adherence of patients. Furthermore, wearable electrochemical drug monitoring devices can also serve as powerful screening tools in the hands of law enforcement agents to combat drug trafficking and support on-site forensic investigations. The review covers various wearable form factors developed for noninvasive monitoring of therapeutic drugs in different body fluids and toward on-site screening of drugs of abuse. The future prospects of such wearable drug monitoring devices are presented with the ultimate goals of introducing accurate real-time drug monitoring protocols and autonomous closed-loop platforms toward precise dose regulation and optimal therapeutic outcomes. Finally, current unmet challenges and existing gaps are discussed for motivating future technological innovations regarding personalized therapy. The current pace of developments and the tremendous market opportunities for such wearable drug monitoring platforms are expected to drive intense future research and commercialization efforts.

Inflammation is a major regulator of drug metabolizing enzymes and transporters: Consequences for the personalization of drug treatment

Inflammation is an evolutionary process that allows survival against acute infection or injury. Inflammation is also a pathophysiological condition shared by numerous chronic diseases. In addition, inflammation modulates important drug-metabolizing enzymes and transporters (DMETs), thus contributing to intra- and interindividual variability of drug exposure. A better knowledge of the impact of inflammation on drug metabolism and its related clinical consequences would help to personalize drug treatment. Here, we summarize the kinetics of inflammatory mediators and the underlying transcriptional and post-transcriptional mechanisms by which they contribute to the inhibition of important DMETs. We also present an updated overview of the effect of inflammation on the pharmacokinetic parameters of most of the drugs that are DMET substrates, for which therapeutic drug monitoring is recommended. Furthermore, we provide opinions on how to integrate the inflammatory status into pharmacogenetics, therapeutic drug monitoring, and population pharmacokinetic strategies to improve the personalization of drug treatment for each patient.

The Steps to Therapeutic Drug Monitoring: A Structured Approach Illustrated With Imatinib

Pharmacometric methods have hugely benefited from progress in analytical and computer sciences during the past decades, and play nowadays a central role in the clinical development of new medicinal drugs. It is time that these methods translate into patient care through therapeutic drug monitoring (TDM), due to become a mainstay of precision medicine no less than genomic approaches to control variability in drug response and improve the efficacy and safety of treatments. In this review, we make the case for structuring TDM development along five generic questions: 1) Is the concerned drug a candidate to TDM? 2) What is the normal range for the drug's concentration? 3) What is the therapeutic target for the drug's concentration? 4) How to adjust the dosage of the drug to drive concentrations close to target? 5) Does evidence support the usefulness of TDM for this drug? We exemplify this approach through an overview of our development of the TDM of imatinib, the very first targeted anticancer agent. We express our position that a similar story shall apply to other drugs in this class, as well as to a wide range of treatments critical for the control of various life-threatening conditions. Despite hurdles that still jeopardize progress in TDM, there is no doubt that upcoming technological advances will shape and foster many innovative therapeutic monitoring methods.

High Content Solid Dispersions for Dose Window Extension: A Basis for Design Flexibility in Fused Deposition Modelling

PURPOSE

This study uses high drug content solid dispersions for dose window extension beyond current demonstrations using fused deposition modelling (FDM) to; i) accommodate pharmaceutically relevant doses of drugs of varying potencies at acceptable dosage form sizes and ii) enable enhanced dose flexibility via modular dosage form design concepts.

METHODS

FDM was used to generate ~0.5 mm thick discs of varying diameter (2-10 mm) from melt-extruded feedstocks based on 10% to 50% w/w felodipine in ethyl cellulose. Drug content was determined by UV spectroscopy and dispensing precision from printed disc mass.

RESULTS

Mean felodipine content was within ±5% of target values for all print volumes and compositions including contents as high as ~50% w/w. However, poor dispensing precision was evident at all print volumes.

CONCLUSIONS

In pursuit of dose flexibility, this successful demonstration of dose window extension using high content solid dispersions preserves FDM design flexibility by maintaining applicability to drugs of varying potencies. The achieved uniformity of content supports the application of varying content solid dispersions to modular dosage form concepts to enhance dose flexibility. However, poor dispensing precision impedes its utilisation until appropriate compatibility between FDM hardware and materials at varying drug contents can be attained.

Extrusion-Based 3D Printing for Pharmaceuticals: Contemporary Research and Applications

Three-dimensional printing (3DP) has a significant impact on organ transplant, cosmetic surgery, surgical planning, prosthetics and other medical fields. Recently, 3 DP attracted the attention as a promising method for the production of small-scale drug production. The knowledge expansion about the population differences in metabolism and genetics grows the need for personalised medicine substantially. In personalised medicine, the patient receives a tailored dose and the release profile is based on his pharmacokinetics data. 3 DP is expected to be one of the leading solutions for the personalisation of the drug dispensing. This technology can fabricate a drug-device with complicated geometries and fillings to obtain the needed drug release profile. The extrusionbased 3 DP is the most explored method for investigating the feasibility of the technology to produce a novel dosage form with properties that are difficult to achieve using the conventional industrial methods. Extrusionbased 3 DP is divided into two techniques, the semi-solid extrusion (SSE) and the fused deposition modeling (FDM). This review aims to explain the extrusion principles behind the two techniques and discuss their capabilities to fabricate novel dosage forms. The advantages and limitations observed through the application of SSE and FDM for fabrication of drug dosage forms were discussed in this review. Further exploration and development are required to implement this technology in the healthcare frontline for more effective and personalised treatment.

Target-Controlled Continuous Infusion for Antibiotic Dosing: Proof-of-Principle in an In-silico Vancomycin Trial in Intensive Care Unit Patients

OBJECTIVES

In this in-silico study, we investigate the clinical utility of target-controlled infusion for antibiotic dosing in an intensive care unit setting using vancomycin as a model compound. We compared target-controlled infusion and adaptive target-controlled infusion, which combines target-controlled infusion with data from therapeutic drug monitoring, with conventional (therapeutic drug monitoring-based) vancomycin dosing strategies.

METHODS

A clinical trial simulation was conducted. This simulation was based on a comprehensive database of clinical records of intensive care unit patients and a systematic review of currently available population-pharmacokinetic models for vancomycin in intensive care unit patients. Dosing strategies were compared in terms of the probability of achieving efficacious concentrations as well as the potential for inducing toxicity.

RESULTS

Adaptive target-controlled infusion outperforms rule-based dosing guidelines for vancomycin. In the first 48 h of treatment, the probability of target attainment is significantly higher for adaptive target-controlled infusion than for the second-best method (Cristallini). Probability of target attainments of 54 and 72% and 47 and 59% for both methods after 24 and 48 h, respectively. Compared to the Cristallini method, which is characterized by a probability of attaining concentrations above 30 mg.L^-1 > 65% in the first few hours of treatment, adaptive target-controlled infusion shows negligible time at risk and a probability of attaining concentrations above 30 mg.L^-1 not exceeding 25%. Finally, in contrast to the other methods, the performance of target-controlled infusion is consistent across subgroups within the population.

CONCLUSIONS

Our study shows that adaptive target-controlled infusion has the potential to become a practical tool for patient-tailored antibiotic dosing in the intensive care unit.

Seconds-resolved pharmacokinetic measurements of the chemotherapeutic irinotecan in situ in the living body

The ability to measure drugs in the body rapidly and in real time would advance both our understanding of pharmacokinetics and our ability to optimally dose and deliver pharmacological therapies. To this end, we are developing electrochemical aptamer-based (E-AB) sensors, a seconds-resolved platform technology that, as critical for performing measurements in vivo, is reagentless, reversible, and selective enough to work when placed directly in bodily fluids. Here we describe the development of an E-AB sensor against irinotecan, a member of the camptothecin family of cancer chemotherapeutics, and its adaptation to in vivo sensing. To achieve this we first re-engineered (via truncation) a previously reported DNA aptamer against the camptothecins to support high-gain E-AB signaling. We then co-deposited the modified aptamer with an unstructured, redox-reporter-modified DNA sequence whose output was independent of target concentration, rendering the sensor's signal gain a sufficiently strong function of square-wave frequency to support kinetic-differential-measurement drift correction. The resultant, 200 μm-diameter, 3 mm-long sensor achieves 20 s-resolved, multi-hour measurements of plasma irinotecan when emplaced in the jugular veins of live rats, thus providing an unprecedentedly high-precision view into the pharmacokinetics of this class of chemotherapeutics.

Spatial, Temporal, and Dose Control of Drug Delivery using Noninvasive Magnetic Stimulation

Noninvasive stimuli-responsive drug delivery using magnetic fields in conjunction with superparamagnetic nanoparticles offers the potential for the spatial and temporal control of drug release. When hyperthermia is not desired and control of the dosage is required, it is necessary to design a platform in which local heating on the nanoscale releases the therapeutic cargo without the bulk heating of the surrounding medium. In this paper, we report a design using a stimuli-responsive nanoparticle platform to control the dosage of the cargo released by an alternating magnetic field (AMF) actuation. A core@shell structure with a superparamagnetic doped iron oxide (MnFe₂O₄@CoFe₂O₄) nanoparticle core in a mesoporous silica shell was synthesized. The core used here has a high saturation magnetization value and a high specific loss power for heat generation under an AMF. The mesoporous shell has a high cargo-carrying capacity. A thermoresponsive molecular-based gatekeeper containing an aliphatic azo group was modified on the core@shell nanoparticles to regulate the cargo release. The mesoporous structure of the silica shell remained intact after exposure to an AMF, showing that the release of cargo is due to the removal of the gatekeepers instead of the destruction of the structure. Most importantly, we demonstrated that the amount of cargo released could be adjusted by the AMF exposure time. By applying multiple sequential exposures of AMF, we were able to release the cargo step-wise and increase the total amount of released cargo. In vitro studies showed that the death of pancreatic cancer cells treated by drug-loaded nanoparticles was controlled by different lengths of AMF exposure time due to different amount of drugs released from the carriers. The strategy developed here holds great promise for achieving the dosage, temporal, and spatial control of therapeutics delivery without the risk of overheating the particles' surroundings.

Drug-physiology interaction and its influence on the QT prolongation-mechanistic modeling study

The current study is an example of drug-disease interaction modeling where a drug induces a condition which can affect the pharmacodynamics of other concomitantly taken drugs. The electrophysiological effects of hypokaliemia and heart rate changes induced by the antiasthmatic drugs were simulated with the use of the cardiac safety simulator. Biophysically detailed model of the human cardiac physiology-ten Tusscher ventricular cardiomyocyte cell model-was employed to generate pseudo-ECG signals and QTc intervals for 44 patients from four clinical studies. Simulated and observed mean QTc values with standard deviation (SD) for each reported study point were compared and differences were analyzed with Student's t test (α = 0.05). The simulated results reflected the QTc interval changes measured in patients, as well as their clinically observed interindividual variability. The QTc interval changes were highly correlated with the change in plasma potassium both in clinical studies and in the simulations (Pearson's correlation coefficient > 0.55). The results suggest that the modeling and simulation approach could provide valuable quantitative insight into the cardiological effect of the potassium and heart rate changes caused by electrophysiologically inactive, non-cardiological drugs. This allows to simulate and predict the joint effect of several risk factors for QT prolongation, e.g., drug-dependent QT prolongation due to the ion channels inhibition and the current patient physiological conditions.

Prediction of olanzapine exposure in individual patients using physiologically based pharmacokinetic modelling and simulation

AIM

The aim of the present study was to predict olanzapine (OLZ) exposure in individual patients using physiologically based pharmacokinetic modelling and simulation (PBPK M&S).

METHODS

A 'bottom-up' PBPK model for OLZ was constructed in Simcyp® (V14.1) and validated against pharmacokinetic studies and data from therapeutic drug monitoring (TDM). The physiological, demographic and genetic attributes of the 'healthy volunteer population' file in Simcyp® were then individualized to create 'virtual twins' of 14 patients. The predicted systemic exposure of OLZ in virtual twins was compared with measured concentration in corresponding patients. Predicted exposures were used to calculate a hypothetical decrease in exposure variability after OLZ dose adjustment.

RESULTS

The pharmacokinetic parameters of OLZ from single-dose studies were accurately predicted in healthy Caucasians [mean-fold errors (MFEs) ranged from 0.68 to 1.14], healthy Chinese (MFEs 0.82 to 1.18) and geriatric Caucasians (MFEs 0.55 to 1.30). Cumulative frequency plots of trough OLZ concentration were comparable between the virtual population and patients in a TDM database. After creating virtual twins in Simcyp®, the R2 values for predicted vs. observed trough OLZ concentrations were 0.833 for the full cohort of 14 patients and 0.884 for the 7 patients who had additional cytochrome P450 2C8 genotyping. The variability in OLZ exposure following hypothetical dose adjustment guided by PBPK M&S was twofold lower compared with a fixed-dose regimen - coefficient of variation values were 0.18 and 0.37, respectively.

CONCLUSIONS

Olanzapine exposure in individual patients was predicted using PBPK M&S. Repurposing of available PBPK M&S platforms is an option for model-informed precision dosing and requires further study to examine clinical potential.

Real Patient and its Virtual Twin: Application of Quantitative Systems Toxicology Modelling in the Cardiac Safety Assessment of Citalopram

A quantitative systems toxicology (QST) model for citalopram was established to simulate, in silico, a 'virtual twin' of a real patient to predict the occurrence of cardiotoxic events previously reported in patients under various clinical conditions. The QST model considers the effects of citalopram and its most notable electrophysiologically active primary (desmethylcitalopram) and secondary (didesmethylcitalopram) metabolites, on cardiac electrophysiology. The in vitro cardiac ion channel current inhibition data was coupled with the biophysically detailed model of human cardiac electrophysiology to investigate the impact of (i) the inhibition of multiple ion currents (I_Kr, I_Ks, I_CaL); (ii) the inclusion of metabolites in the QST model; and (iii) unbound or total plasma as the operating drug concentration, in predicting clinically observed QT prolongation. The inclusion of multiple ion channel current inhibition and metabolites in the simulation with unbound plasma citalopram concentration provided the lowest prediction error. The predictive performance of the model was verified with three additional therapeutic and supra-therapeutic drug exposure clinical cases. The results indicate that considering only the hERG ion channel inhibition of only the parent drug is potentially misleading, and the inclusion of active metabolite data and the influence of other ion channel currents should be considered to improve the prediction of potential cardiac toxicity. Mechanistic modelling can help bridge the gaps existing in the quantitative translation from preclinical cardiac safety assessment to clinical toxicology. Moreover, this study shows that the QST models, in combination with appropriate drug and systems parameters, can pave the way towards personalised safety assessment.