Physiologically Based Pharmacokinetic Modelling to Identify Pharmacokinetic Parameters Driving Drug Exposure Changes in the Elderly

Utility of life stage-specific chemical risk assessments based on New Approach Methodologies (NAMs)

The safety assessments for chemicals targeted for use or expected to be exposed to specific life stages, including infancy, childhood, pregnancy and lactation, and geriatrics, need to account for extrapolation of data from healthy adults to these populations to assess their human health risk. However, often adequate and relevant toxicity or pharmacokinetic (PK) data of chemicals in specific life stages are not available. For such chemicals, New Approach Methodologies (NAMs), such as physiologically based pharmacokinetic (PBPK) modeling, biologically based dose response (BBDR) modeling, in vitro to in vivo extrapolation (IVIVE), etc. can be used to understand the variability of exposure and effects of chemicals in specific life stages and assess their associated risk. A life stage specific PBPK model incorporates the physiological and biochemical changes associated with each life stage and simulates their impact on the absorption, distribution, metabolism, and elimination (ADME) of these chemicals. In our review, we summarize the parameterization of life stage models based on New Approach Methodologies (NAMs) and discuss case studies that highlight the utility of a life stage based PBPK modeling for risk assessment. In addition, we discuss the utility of artificial intelligence (AI)/machine learning (ML) and other computational models, such as those based on in vitro data, as tools for estimation of relevant physiological or physicochemical parameters and selection of model. We also discuss existing gaps in the available toxicological datasets and current challenges that need to be overcome to expand the utility of NAMs for life stage-specific chemical risk assessment.

Ageing-related considerations for medication used in supportive care in cancer

Both randomized controlled trials (RCTs) and retrospective studies have shown that a comprehensive geriatric assessment (CGA) prior to a patient commencing systemic anti-cancer therapy (SACT) results in improved quality of life outcomes and is associated with a decreased risk of grade 3-5 toxicity; however, data are lacking in relation to adverse drug events (ADE) associated with supportive care medications. Supportive care medications are prescribed as prophylactic agents in a SACT regimen, for management of treatment related toxicity and for symptoms caused by the disease itself. While necessary, the commencement of SACT and supportive medications may cause, or exacerbate, a significant drug burden in older patients, some of whom may have existing comorbidities. For many medications, older adults are underrepresented in pharmacokinetic and pharmacodynamic modelling studies. In this article we will review ageing-related changes in pharmacokinetics and pharmacodynamics, as well as how these changes may impact supportive care medications. Additional considerations for prescribing these medications in older adults with cancer, such as polypharmacy, potentially inappropriate medications, drug-drug interactions, and anticholinergic burden, as well as ageing-related considerations and recommendations for supportive care medications commonly used in older adults with cancer are also reviewed.

Central Nervous System Medications: Pharmacokinetic and Pharmacodynamic Considerations for Older Adults

Most drugs have not been evaluated in the older population. Recognizing physiological alterations associated with changes in drug disposition and with the ultimate effect, especially in central nervous system-acting drugs, is fundamental. While considering pharmacokinetics, it should be noted that the absorption of most drugs from the gastrointestinal tract does not change in advanced age. There are only few data about the effect of age on the transdermal absorption of medications such as fentanyl. Absorption from an intramuscular injection may be similar in older adults as in younger patients. The distribution of lipophilic drugs (such as diazepam) is increased owing to a relative increase in the percentage of body fat, causing drug accumulation and prolonged drug elimination following cessation. Phase I drug biotransformation is variably decreased in aging, impacting elimination, and hepatic drug clearance has been shown to decrease in older individuals by 10-40% for most drugs studied. Lower doses of phenothiazines, butyrophenones, atypical antipsychotics, antidepressants (citalopram, mirtazapine, and tricyclic antidepressants), and benzodiazepines (such as diazepam) achieve the same extent of exposure. For renally cleared drugs with no prior metabolism (such as gabapentin), the glomerular filtration rate appropriately estimates drug clearance. Important pharmacodynamic changes in older adults include an increased sedative effect of benzodiazepines at a given drug exposure, and a higher sensitivity to mu opiate receptor agonists and to opioid adverse effects. Artificial intelligence, physiologically based pharmacokinetic modeling and simulation, and concentration-effect modeling enabling a differentiation between the pharmacokinetic and the pharmacodynamic effects of aging might help to close some of the gaps in knowledge.

Evaluation of Age-Related Changes in Teneligliptin Pharmacokinetics in Japanese and European Descent Subjects Using a Physiologically Based Pharmacokinetic Model

INTRODUCTION

Drugs often show differing pharmacokinetic (PK) profiles, such as higher plasma concentrations, in older people than in younger people owing to age-related decreases in physiological functions. However, it is difficult to evaluate the PK in older populations. Therefore, we simulated the plasma age-related changes in the PK of teneligliptin, a dipeptidyl peptidase-4 inhibitor, using physiologically based PK (PBPK) models.

METHODS

The previously developed PBPK model was revalidated by comparison between simulated data and clinical study data that included older subjects (up to 75 years old). We then simulated the plasma concentration-time profiles for teneligliptin at a dose of 20 mg (single and multiple doses) in virtual Japanese (20-70 years old) and European descent (20-98 years old) subjects. PK parameters were calculated by race and age group.

RESULTS

We confirmed the validity of the previous PBPK model by comparison between simulated data and clinical study data. In the evaluation of age-related changes in PK after single and multiple doses using the PBPK model, the area under the plasma concentration-time curve (AUC) of teneligliptin tended to increase slightly with age in both populations up to 70 years old. However, no clear age-related change in the maximum plasma concentration (Cmax) of teneligliptin was observed. In the European descent subjects aged ≥ 70 years, the AUC tended to increase but the ratio of the change in Cmax was smaller than that in AUC. In both populations, there were positive correlations between AUC and age, but not between Cmax and age.

CONCLUSION

The simulation using a PBPK model showed a tendency for the AUC of teneligliptin to increase with age, whereas Cmax was less affected by age than AUC.

Physiologically based pharmacokinetic modeling of apixaban to predict exposure in populations with hepatic and renal impairment and elderly populations

BACKGROUND

Apixaban is a factor Xa inhibitor with a limited therapeutic index that belongs to the family of oral direct anticoagulants. The pharmacokinetic (PK) behavior of apixaban may be altered in elderly populations and populations with renal or hepatic impairment, necessitating dosage adjustments.

METHODS

This study was conducted to examine how the physiologically based pharmacokinetic (PBPK) model describes the PKs of apixaban in adult and elderly populations and to determine the PKs of apixaban in elderly populations with renal and hepatic impairment. After PBPK models were constructed using the reported physicochemical properties of apixaban and clinical data, they were validated using data from clinical studies involving various dose ranges. Comparing predicted and observed blood concentration data and PK parameters was utilized to evaluate the model's fit performance.

RESULTS

Doses should be reduced to approximately 70% of the healthy adult population for the healthy elderly population to achieve the same PK exposure; approximately 88%, 71%, and 89% of that for the elderly populations with mild, moderate, and severe renal impairment, respectively; and approximately 96%, 81%, and 58% of that for the Child Pugh-A, Child Pugh-B, and Child Pugh-C hepatic impairment elderly populations, respectively to achieve the same PK exposure.

CONCLUSION

The findings indicate that the renal and hepatic function might be considered for apixaban therapy in Chinese elderly patients and the PBPK model can be used to optimize dosage regimens for specific populations.

Physiologically based pharmacokinetic modeling of candesartan to predict the exposure in hepatic and renal impairment and elderly populations

Background

Candesartan cilexetil is a widely used angiotensin II receptor blocker with minimal adverse effects and high tolerability for the treatment of hypertension. Candesartan is administered orally as the prodrug candesartan cilexetil, which is wholly and swiftly converted to the active metabolite candesartan by carboxylesterase during absorption in the intestinal tract. In populations with renal or hepatic impairment, candesartan's pharmacokinetic (PK) behavior may be altered, necessitating dosage adjustments.

Objectives

This study was conducted to examine how the physiologically based PK (PBPK) model characterizes the PKs of candesartan in adult and geriatric populations and to predict the PKs of candesartan in elderly populations with renal and hepatic impairment.

Design

After developing PBPK models using the reported physicochemical properties of candesartan and clinical data, these models were validated using data from clinical investigations involving various dose ranges.

Methods

Comparing predicted and observed blood concentration data and PK parameters was used to assess the fit performance of the models.

Results

Doses should be reduced to approximately 94% of Chinese healthy adults for the Chinese healthy elderly population; approximately 92%, 68%, and 64% of that of the Chinese healthy adult dose in elderly populations with mild, moderate, and severe renal impairment, respectively; and approximately 72%, 71%, and 52% of that of the Chinese healthy adult dose in elderly populations with Child-Pugh-A, Child-Pugh-B, and Child-Pugh-C hepatic impairment, respectively.

Conclusion

The results suggest that the PBPK model of candesartan can be utilized to optimize dosage regimens for special populations.

Inclusion of functional measures and frailty in the development and evaluation of medicines for older adults

The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) E7, the guidance for the conduct of clinical trials in people older than age 65 years, dates from 1994. Since then, the inclusion of older people in clinical trials has hardly improved, particularly for the oldest old age group (individuals older than age 75 years), which is the fastest growing demographic bracket in the EU. Even though most medications are taken by this group, relevant endpoints and safety outcomes for this cohort are rarely included and reported, both in clinical trials and regulatory approval documents. To improve the critical appraisal and the regulatory review of medicines taken by frail older adults, eight recommendations are presented and discussed in this Health Policy. These recommendations are brought together from different perspectives and experience of the treatment of older patients. On one side, the perspective of medical practitioners from various clinical disciplines, with their direct experience of clinical decision making; on the other, the perspective of regulators assessing the data submitted in medicine registration dossiers, their relevance to the risk-benefit balance for older patients, and the communication of the findings in the product information. Efforts to improve the participation of older people in clinical trials have been in place for more than a decade, with little success. The recommendations presented here are relevant for stakeholders, authorities, pharmaceutical companies, and researchers alike, as the implementation of these measures is not under the capacity of a single entity. Improving the inclusion of frail older adults requires awareness, focus, and action on the part of those who can effect a much needed change.

Apixaban and rivaroxaban's physiologically-based pharmacokinetic model validation in hospitalized patients: A first step for larger use of a priori modeling approach at bed side

When used in real-world conditions, substantial interindividual variations in direct oral anticoagulant (DOAC) plasma concentrations are observed for a given dose, leading to a risk of over- or under-exposure and clinically significant adverse events. Physiologically-based pharmacokinetic (PBPK) models could help physicians to tailor DOAC prescriptions in vulnerable patient populations, such as those in the hospital setting. The present study aims to validate prospectively PBPK models for rivaroxaban and apixaban in a large cohort of elderly, polymorbid, and hospitalized patients. In using a model of geriatric population integrating appropriate physiological parameters into models first optimized with healthy volunteer data, observed plasma concentration collected in hospitalized patients on apixaban (n = 100) and rivaroxaban (n = 100) were adequately predicted (ratio predicted/observed area under the concentration curve for a dosing interval [AUCtau ] = 0.97 [0.96-0.99] geometric mean, 90% confidence interval, ratio predicted/observed AUCtau  = 1.03 [1.02-1.05]) for apixaban and rivaroxaban, respectively. Validation of the present PBPK models for rivaroxaban and apixaban in in-patients represent an additional step toward the feasibility of bedside use.

In silico identification of 1,2,4-triazoles as potential Candida Albicans inhibitors using 3D-QSAR, molecular docking, molecular dynamics simulations, and ADMET profiling

Fluconazole and Voriconazole are individual antifungal inhibitors broadly adopted for treating fungal infections, including Candida Albicans. Unfortunately, these medicines clinically used have significant side effects. Consequently, the improvement of safer and better therapy became more indispensable. In this study, a set of 27 1,2,4-triazole compounds have been tested as potential Candida Albicans inhibitors by using different theoretical methods. The created comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) contour maps significantly impacted the development of novel Candida Albicans inhibitors with valuable activities. The mode of interactions between the 1,2,4-triazole inhibitors and the targeted receptor was studied by molecular docking simulation. The proposed new molecule P1 showed satisfied stability in the active pocket of the targeted receptor compared to the more active molecule in the dataset compared to Fluconazole medication. Meanwhile, the binding energy obtained by molecular docking for molecule P1 is - 9.3 kcal/mol compared with - 6.7 kcal/mol for Fluconazole medication. Also, MM/GBSA value obtained by molecular dynamics simulations at 100 ns for molecule P1 is - 33.34 kcal/mol compared with - 15.85 kcal/mol for Fluconazole medication. In addition, molecule P1 showed good oral bioavailability and was non-toxic according to ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. Therefore, the results indicated compound P1 might be a future inhibitor of Candida Albicans infection.

Physiologically Based Pharmacokinetic Model To Predict Metoprolol Disposition in Healthy and Disease Populations

The evolution in the development of drugs has increased the popularity of physiologically based pharmacokinetic (PBPK) models. This study seeks to assess the PK of metoprolol in populations with healthy, chronic kidney disease (CKD), and acute myocardial infarction (AMI) conditions by developing and evaluating PBPK models. An extensive literature review for identifying and selecting plasma concentration vs time profile data and other drug-related parameters was undergone for their integration into the PK-Sim program followed by the development of intravenous, oral, and diseased models. The developed PBPK model of metoprolol was then evaluated using the visual predictive checks, mean observed/predicted ratios (Robs/pre), and average fold error for all PK parameters, i.e., the area under the curve (AUC), maximal plasma concentration, and clearance. The model evaluation depicted that none of the PK parameters were out of the allowed range (2-fold error) in the case of the mean Robs/pre ratios. The model anticipations were executed to determine the influence of diseases on unbound and total AUC after the application of metoprolol in healthy, moderate, and severe CKD. The dosage reductions were also suggested based on differences in unbound and total AUC in different stages of CKD. The developed PBPK models have successfully elaborated the PK changes of metoprolol occurring in healthy individuals and those with renal and heart diseases (CKD & AMI), which may be fruitful for dose optimization among diseased patients.

The investigation of the complex population-drug-drug interaction between ritonavir-boosted lopinavir and chloroquine or ivermectin using physiologically-based pharmacokinetic modeling

OBJECTIVES

Therapy failure caused by complex population-drug-drug (PDDI) interactions including CYP3A4 can be predicted using mechanistic physiologically-based pharmacokinetic (PBPK) modeling. A synergy between ritonavir-boosted lopinavir (LPVr), ivermectin, and chloroquine was suggested to improve COVID-19 treatment. This work aimed to study the PDDI of the two CYP3A4 substrates (ivermectin and chloroquine) with LPVr in mild-to-moderate COVID-19 adults, geriatrics, and pregnancy populations.

METHODS

The PDDI of LPVr with ivermectin or chloroquine was investigated. Pearson's correlations between plasma, saliva, and lung interstitial fluid (ISF) levels were evaluated. Target site (lung epithelial lining fluid [ELF]) levels of ivermectin and chloroquine were estimated.

RESULTS

Upon LPVr coadministration, while the chloroquine plasma levels were reduced by 30, 40, and 20%, the ivermectin plasma levels were increased by a minimum of 425, 234, and 453% in adults, geriatrics, and pregnancy populations, respectively. The established correlation equations can be useful in therapeutic drug monitoring (TDM) and dosing regimen optimization.

CONCLUSIONS

Neither chloroquine nor ivermectin reached therapeutic ELF levels in the presence of LPVr despite reaching toxic ivermectin plasma levels. PBPK modeling, guided with TDM in saliva, can be advantageous to evaluate the probability of reaching therapeutic ELF levels in the presence of PDDI, especially in home-treated patients.

Physiologically Based Pharmacokinetic Modelling to Identify Physiological and Drug Parameters Driving Pharmacokinetics in Obese Individuals

BACKGROUND

Obese individuals are often underrepresented in clinical trials, leading to a lack of dosing guidance.

OBJECTIVE

This study aimed to investigate which physiological parameters and drug properties determine drug disposition changes in obese using our physiologically based pharmacokinetic (PBPK) framework, informed with obese population characteristics.

METHODS

Simulations were performed for ten drugs with clinical data in obese (i.e., midazolam, triazolam, caffeine, chlorzoxazone, acetaminophen, lorazepam, propranolol, amikacin, tobramycin, and glimepiride). PBPK drug models were developed and verified first against clinical data in non-obese (body mass index (BMI) ≤ 30 kg/m²) and subsequently in obese (BMI ≥ 30 kg/m²) without changing any drug parameters. Additionally, the PBPK model was used to study the effect of obesity on the pharmacokinetic parameters by simulating drug disposition across BMI, starting from 20 up to 60 kg/m².

RESULTS

Predicted pharmacokinetic parameters were within 1.25-fold (71.5%), 1.5-fold (21.5%) and twofold (7%) of clinical data. On average, clearance increased by 1.6% per BMI unit up to 64% for a BMI of 60 kg/m², which was explained by the increased hepatic and renal blood flows. Volume of distribution increased for all drugs up to threefold for a BMI of 60 kg/m²; this change was driven by pK_a for ionized drugs and logP for neutral and unionized drugs. C_max decreased similarly across all drugs while t_max remained unchanged.

CONCLUSION

Both physiological changes and drug properties impact drug pharmacokinetics in obese subjects. Clearance increases due to enhanced hepatic and renal blood flows. Volume of distribution is higher for all drugs, with differences among drugs depending on their pK_a/logP.

Adaptation and factor structure of three psychometric instruments measuring behavioral aspects of medication prescribing in physician assistants

OBJECTIVES

(1) Present the factor structure of two psychometric instruments for self-efficacy and one for outcome expectations of medication prescribing; (2) evaluate the reliability of the scales, and (3) present preliminary evidence of validity.

METHODS

Physician assistants (PA) and PA students completed a survey evaluating three psychometric instruments: (1) Self-Efficacy in Prescribing (SEP), (2) Self-Efficacy in Prescribing-Geriatric (SEPG), and (3) Outcomes Expectations of Prescribing Errors (OEP). Students also evaluated 3 hypothetical prescriptions, two of which contained a prescribing error. Students were instructed to identify (1) if an error occurred and (2) what type of error. The data were analyzed using parallel analysis with a varimax rotation, Cronbach's α, Pearson and Spearman correlations.

RESULTS

One hundred eighty five (n = 185) respondents completed the survey (response rate = 63.8%). The parallel analysis found that the SEP had one 7-item factor with α = 0.94 (M = 5.7 (SD = 1.9) out of 10). The SEPG also had one 7-item factor with α = 0.95 (M = 5.5 (1.9). The OEP had one 6-item factor with α = 0.89 (M = 3.5 (SD = 0.8) out of 5). The SEP and SEPG, were correlated to the OEP each other (both p < 0.01). Actively practicing PAs had the highest composite mean SEP and SEPG scores. First-year PA students had the highest mean scores for the OEP. There was a weak association between the mean SEPG score and the number of correctly identified prescriptions (r_s = 0.18, p = 0.04).

CONCLUSION

The SEP, SEPG, and OEP show preliminary evidence of reliability and structural, construct, and known-group validities using simulated prescriptions. These tools may be able to be used by educators and implementation scientists as one method to show the effectiveness of future interventions to reduce incidence of prescribing errors.

Simulation of Febuxostat Pharmacokinetics in Healthy Subjects and Patients with Impaired Kidney Function Using Physiologically Based Pharmacokinetic Modeling

Febuxostat is recommend by the American College of Rheumatology Gout Management Guidelines as a first-line therapy for lowering the level of urate in patients with gout. At present, this drug is being prescribed mainly based on the clinical experience of doctors. The potential effects of clinical and demographic variables on the bioavailability and therapeutic effectiveness of febuxostat are not being considered. In this study, a physiologically based pharmacokinetic (PBPK) model of febuxostat was developed, thereby providing a theoretical basis for the individualized dosing of this drug in gout patients. The plasma concentration-time profiles corresponding to healthy subjects and gout patients with normal kidney function were simulated and validated; then, the model was used to predict the pharmacokinetic (PK) data of the drug in gout patients suffering from varying degrees of impaired kidney function. The error values (the predicted value/observed value) were used to validate the simulated PK parameters predicted by the PBPK model, including the area under the plasma concentration-time curve, the maximum plasma concentration, and time to maximum plasma concentration. Considering that to all error fold changes were smaller than 2 the PBPK model was. In subjects suffering from mild kidney impairment, moderate kidney impairment, severe kidney impairment, and end-stage kidney disease (ESRD), the predicted AUC_0-24h values increased by 1.62, 1.74, 2.27, and 2.65-fold, respectively, compared to gout patients with normal kidney function. Overall, the results showed that the PBPK model constructed in this study predict the pharmacokinetic changes in gout patients suffering from varying degrees of impaired kidney function. This article is protected by copyright. All rights reserved.

Impact of the CYP2D6 Genotype on Metoprolol Tolerance and Adverse Events in Elderly Chinese Patients With Cardiovascular Diseases

The latest consensus has changed CYP2D6 genotyping among Chinese population, while its impact on metoprolol tolerance and adverse events in elderly Chinese patients with cardiovascular diseases remains unclear. In this study, we prospectively included elderly patients who started metoprolol treatment for cardiovascular indications. According to the latest consensus on CYP2D6 genotype-to-phenotype translation, the patients were categorized as normal, intermediate, or poor metabolizers (NMs, IMs, or PMs, respectively) by detecting the presence of the CYP2D6*1, *2, *5, *10, and *14. Logistic regression model was used to analyze the correlation between the CYP2D6 phenotype and incidence of adverse events, which were assessed over a 12-week period. In this study, there were 651 (62.7%) NMs, 385 (37.1%) IMs, and 3 (0.3%) PMs. After 12 weeks of follow-up, compared with NMs, IMs had the lower maintenance dose [50.0 (25.0–50.0) mg/day vs. 25.0 (25.0–50.0) mg/day, p < 0.001] and lower weight-adjusted maintenance doses (0.52 ± 0.25 mg/day/kg vs. 0.42 ± 0.22 mg/day/kg, p < 0.001), and had higher incidence of postural hypotension (6.0% vs. 10.9%, p = 0.006), bradycardia (21.5% vs. 28.6%, p = 0.011), asystole (0.8% vs. 3.1%, p = 0.009) and syncope (2.0% vs. 6.2%, p = 0.001). In logistic regression model, the overall incidence of adverse events was 1.37-fold larger in IMs than in NMs (odds ratio = 1.37, 95% confidence interval = 1.05–1.79, p = 0.021). We conclude that IMs have lower tolerance and higher incidence of metoprolol-related adverse events than NMs in elderly Chinese patients with cardiovascular diseases. CYP2D6 genotyping is justifiable in elderly patients to minimize the risk of adverse events and ensure the benefits of metoprolol.

Magnitude of Drug–Drug Interactions in Special Populations

Drug–drug interactions (DDIs) are one of the most frequent causes of adverse drug reactions or loss of treatment efficacy. The risk of DDIs increases with polypharmacy and is therefore of particular concern in individuals likely to present comorbidities (i.e., elderly or obese individuals). These special populations, and the population of pregnant women, are characterized by physiological changes that can impact drug pharmacokinetics and consequently the magnitude of DDIs. This review compiles existing DDI studies in elderly, obese, and pregnant populations that include a control group without the condition of interest. The impact of physiological changes on the magnitude of DDIs was then analyzed by comparing the exposure of a medication in presence and absence of an interacting drug for the special population relative to the control population. Aging does not alter the magnitude of DDIs as the related physiological changes impact the victim and perpetrator drugs to a similar extent, regardless of their elimination pathway. Conversely, the magnitude of DDIs can be changed in obese individuals or pregnant women, as these conditions impact drugs to different extents depending on their metabolic pathway.

Sex-related pharmacokinetic differences with aging

PURPOSE

The proportion of women increases with advanced age, but older women are often underrepresented in clinical trials. Therefore, little is known about the combined effect of sex- and age-related physiological changes on drug pharmacokinetics.

METHODS

We compiled clinical studies, which investigated sex-related pharmacokinetic differences both in older and young women and men. The ratio women/men was calculated for various pharmacokinetic parameters across adulthood to assess sex-related differences in drug pharmacokinetics with aging. The contribution of body weight and drug characteristics to sex-related pharmacokinetic differences were explored using analysis of variance.

RESULTS

We found 67 studies reporting the pharmacokinetics for 56 drugs both in older and young women and men. Median peak concentration (C_max) (interquartile range (IQR)) and drug exposure (AUC) (IQR) were 22% (8-41%) and 20% (0-39%) higher in women compared with men whereas time to peak concentration (t_max), apparent volume of distribution (VdF) and elimination half-life (t_1/2) were not significantly different. Body weight and the drug main elimination pathway contributed to sex-related differences in C_max and AUC. Relative to men, women had a modest increase in C_max with increasing age (r = 0.19, p = 0.04). Conversely, sex-related differences in AUC remained constant with increasing age.

CONCLUSION

The pharmacokinetic differences between women and men were modest and, with the exception of C_max, remained constant with increasing age. The higher plasma concentration might be correlated to more adverse events in older women and thus, drug treatment should be started on the lower recommended dosage when appropriate particularly for drugs characterized by a narrow therapeutic index.

Variational Gaussian Mixture Models with robust Dirichlet concentration priors for virtual population generation in hypertrophic cardiomyopathy: a comparison study

Nowadays, there is a growing need for the development of computationally efficient virtual population generators for large-scale in-silico clinical trials. In this work, we utilize the Gaussian Mixture Models (GMM) with variational Bayesian inference (BGMM) using robust estimations of Dirichlet concentration priors for the generation of virtual populations. The estimations were based on an exponential transformation of the number of Gaussian components. The proposed method was compared against state-of-the-art virtual data generators, such as, the Bayesian networks, the supervised tree ensembles (STE), the unsupervised tree ensembles (UTE), and the artificial neural networks (ANN) towards the generation of 20000 virtual patients with hypertrophic cardiomyopathy (HCM). Our results suggest that the proposed BGMM can yield virtual distributions with small inter- and intra-correlation difference (0.013 and 0.012), in lower execution time (4.321 sec) than STE which achieved the second-best performance.

Physiologically Based Pharmacokinetic Modelling to Investigate the Impact of the Cytokine Storm on CYP3A Drug Pharmacokinetics in COVID-19 Patients

Patients with coronavirus disease 2019 (COVID‐19) may experience a cytokine storm with elevated interleukin‐6 (IL‐6) levels in response to severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2). IL‐6 suppresses hepatic enzymes, including CYP3A; however, the effect on drug exposure and drug‐drug interaction magnitudes of the cytokine storm and resulting elevated IL‐6 levels have not been characterized in patients with COVID‐19. We used physiologically‐based pharmacokinetic (PBPK) modeling to simulate the effect of inflammation on the pharmacokinetics of CYP3A metabolized drugs. A PBPK model was developed for lopinavir boosted with ritonavir (LPV/r), using clinically observed data from people living with HIV (PLWH). The inhibition of CYPs by IL‐6 was implemented by a semimechanistic suppression model and verified against clinical data from patients with COVID‐19, treated with LPV/r. Subsequently, the verified model was used to simulate the effect of various clinically observed IL‐6 levels on the exposure of LPV/r and midazolam, a CYP3A model drug. Clinically observed LPV/r concentrations in PLWH and patients with COVID‐19 were predicted within the 95% confidence interval of the simulation results, demonstrating its predictive capability. Simulations indicated a twofold higher LPV exposure in patients with COVID‐19 compared with PLWH, whereas ritonavir exposure was predicted to be comparable. Varying IL‐6 levels under COVID‐19 had only a marginal effect on LPV/r pharmacokinetics according to our model. Simulations showed that a cytokine storm increased the exposure of the CYP3A paradigm substrate midazolam by 40%. Our simulations suggest that CYP3A metabolism is altered in patients with COVID‐19 having increased cytokine release. Caution is required when prescribing narrow therapeutic index drugs particularly in the presence of strong CYP3A inhibitors.

An Update on Medication Use in Older Adults: a Narrative Review

Purpose of Review

The global phenomenon of population aging is impacting the health and care needs of society. The use of medications by older adults is acknowledged to be the most common form of medical intervention for many acute and chronic conditions and prescribing in this population continues to increase. In this narrative review, we summarise the age-related factors that should be considered when prescribing for older adults, address some of the perennial challenges related to medicine use in older people, and highlight important emerging research in this area.

Recent Findings

A range of age-related factors should be considered when prescribing for older adults. However, the evidence base still lacks data pertaining to older adults due to their continued under-representation in clinical trials. Multimorbidity, polypharmacy, and inappropriate prescribing continue to remain prevalent among older adults, although recent research has been focused on the development and evaluation of complex interventions to address these challenges.

Summary

Further high-quality studies of interventions to improve and support medication use in older adults are needed, ensuring that older adults are well represented in such trials and consideration is given to the measurement of patient- and provider-focused outcomes.

Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases

The advancement in the processing speeds of computing machines has facilitated the development of complex physiologically based pharmacokinetic (PBPK) models. These PBPK models can incorporate disease-specific data and could be used to predict pharmacokinetics (PK) of administered drugs in different chronic conditions. The present study aimed to develop and evaluate PBPK drug-disease models for captopril after incorporating relevant pathophysiological changes occurring in adult chronic kidney disease (CKD) and chronic heart failure (CHF) populations. The population-based PBPK simulator Simcyp was used as a modeling and simulation platform. The visual predictive checks and mean observed/predicted ratios (ratio_(Obs/pred)) of the PK parameters were used for model evaluation. The developed disease models were successful in predicting captopril PK in all three stages of CKD (mild, moderate, and severe) and CHF, as the observed and predicted PK profiles and the ratio_(obs/pred) for the PK parameters were in close agreement. The developed captopril PBPK models can assist in tailoring captopril dosages in patients with different disease severity (CKD and CHF).

Physiologically-Based Pharmacokinetic Modeling to Support the Clinical Management of Drug-Drug Interactions With Bictegravir

Bictegravir is equally metabolized by cytochrome P450 (CYP)3A and uridine diphosphate‐glucuronosyltransferase (UGT)1A1. Drug–drug interaction (DDI) studies were only conducted for strong inhibitors and inducers, leading to some uncertainty whether moderate perpetrators or multiple drug associations can be safely coadministered with bictegravir. We used physiologically‐based pharmacokinetic (PBPK) modeling to simulate DDI magnitudes of various scenarios to guide the clinical DDI management of bictegravir. Clinically observed DDI data for bictegravir coadministered with voriconazole, darunavir/cobicistat, atazanavir/cobicistat, and rifampicin were predicted within the 95% confidence interval of the PBPK model simulations. The area under the curve (AUC) ratio of the DDI divided by the control scenario was always predicted within 1.25‐fold of the clinically observed data, demonstrating the predictive capability of the used modeling approach. After the successful verification, various DDI scenarios with drug pairs and multiple concomitant drugs were simulated to analyze their effect on bictegravir exposure. Generally, our simulation results suggest that bictegravir should not be coadministered with strong CYP3A and UGT1A1 inhibitors and inducers (e.g., atazanavir, nilotinib, and rifampicin), but based on the present modeling results, bictegravir could be administered with moderate dual perpetrators (e.g., efavirenz). Importantly, the inducing effect of rifampicin on bictegravir was predicted to be reversed with the concomitant administration of a strong inhibitor such as ritonavir, resulting in a DDI magnitude within the efficacy and safety margin for bictegravir (0.5–2.4‐fold). In conclusion, the PBPK modeling strategy can effectively be used to guide the clinical management of DDIs for novel drugs with limited clinical experience, such as bictegravir.

Physiologically-Based Pharmacokinetic Modeling Combined with Swiss HIV Cohort Study Data Supports No Dose Adjustment of Bictegravir in Elderly Individuals Living With HIV

Clinical studies in aging people living with HIV (PLWH) are sparse for the novel integrase inhibitor bictegravir, leading to some uncertainty about dosing recommendations for elderly PLWH. The objective of this study was to investigate the continuous impact of aging on bictegravir pharmacokinetics by combining clinically observed data with modeling to support a safe and efficient anti‐HIV therapy with advanced age. A physiologically‐based pharmacokinetic (PBPK) model was developed for bictegravir with clinically observed data from phase I studies. The predictive model performance was verified using bictegravir plasma concentrations sampled as part of the general therapeutic drug monitoring (TDM) program of the Swiss HIV Cohort Study in young (20–55 years) and elderly PLWH (55–85 years). The verified PBPK model subsequently predicted the continuous impact of aging on bictegravir pharmacokinetics across adulthood (20–99 years). Bictegravir exposure was unchanged in elderly compared with young PLWH when analyzing the TDM data of the Swiss HIV Cohort Study. PBPK simulations predicted clinically observed data from 60 young and 32 elderly PLWH mostly within the 95% confidence interval, demonstrating the predictive power of the used modeling approach. Simulations predicted drug exposure to increase up to 40% during adulthood, which was not statistically significantly different from the age‐related pharmacokinetic changes of other HIV and non‐HIV drugs. Sex had no impact on the age‐related changes of bictegravir pharmacokinetics. Considering the safety margin of bictegravir, a dose adjustment for the novel integrase inhibitor is a priori not necessary in elderly PLWH in the absence of severe comorbidities.

Application of Model Informed Precision Dosing to Address the Impact of Pregnancy Stage and CYP2D6 Phenotype on Foetal Morphine Exposure

Guidance regarding the effect of codeine and its metabolites on foetal development is limited by small studies and inconsistent findings. The primary objective was to use physiologically based pharmacokinetic modelling to investigate the impact of gestational stage and maternal CYP2D6 phenotype on foetal morphine exposure following codeine administration. Full body physiologically based pharmacokinetic models were developed and verified for codeine and morphine using Simcyp (version 19.1). The impact of gestational age and maternal CYP2D6 phenotype on foetal and maternal morphine and codeine exposure following oral codeine administration was modelled in a cohort of 250 pregnant females and foetuses at gestational weeks 0 (mothers only), 6, 12, 24 and 36. Consistent with the known effect on codeine metabolism, a clinically meaningful (> 1.65-fold) increase in foetal morphine AUC was observed in the CYP2D6 UM phenotype cohort compared to the CYP2D6 EM and PM phenotype cohorts. The mean (95% CI) foetal morphine AUC in the CYP2D6 UM cohort of 0.988 (0.902 to 1.073) ng/mL.h was 1.8-fold higher than the CYP2D6 EM cohort of 0.546 (0.492 to 0.600) ng/mL.h (p < 0.001). Despite a 2.8-fold increase in maternal CYP2D6 protein abundance between gestational weeks 6 and 36, the mean foetal morphine AUC in the CYP2D6 EM and UM phenotype cohorts reduced by 1.55- and 1.75-fold, respectively, over this period. Maternal CYP2D6 phenotype is a significant determinant of foetal morphine AUC. Simulations suggest that the greatest risk with respect to foetal morphine exposure is during the first trimester of pregnancy, particularly in CYP2D6 UM phenotype mothers.

Clinical Data Combined With Modeling and Simulation Indicate Unchanged Drug-Drug Interaction Magnitudes in the Elderly

Age-related comorbidities and consequently polypharmacy are highly prevalent in the elderly, resulting in an increased risk for drug-drug interactions (DDIs). The effect of aging on DDI magnitudes is mostly uncertain, leading to missing guidance regarding the clinical DDI management in the elderly. Clinical data obtained in aging people living with HIV ≥ 55 years, who participated in the Swiss HIV Cohort Study, demonstrated unchanged DDI magnitudes with advanced aging for four studied DDI scenarios. These data plus published data for midazolam in the presence of clarithromycin and rifampicin in elderly individuals assessed the predictive potential of the used physiologically-based pharmacokinetic (PBPK) model to simulate DDIs in the elderly. All clinically observed data were generally predicted within the 95% confidence interval of the PBPK simulations. The verified model predicted subsequently the magnitude of 50 DDIs across adulthood (20-99 years) with 42 scenarios being only verified in adults aged 20-50 years in the absence of clinically observed data in the elderly. DDI magnitudes were not impacted by aging regardless of the involved drugs, DDI mechanism, mediators of DDIs, or the sex of the investigated individuals. The prediction of unchanged DDI magnitudes with advanced aging were proofed by 17 published, independent DDIs that were investigated in young and elderly subjects. In conclusion, this study demonstrated by combining clinically observed data with modeling and simulation that aging does not impact DDI magnitudes and thus, clinical management of DDIs can a priori be similar in aging men and women in the absence of severe comorbidities.

Effect of Systemic Inflammatory Response to SARS-CoV-2 on Lopinavir and Hydroxychloroquine Plasma Concentrations

Coronavirus disease 2019 (COVID-19) leads to inflammatory cytokine release, which can downregulate the expression of metabolizing enzymes. This cascade affects drug concentrations in the plasma. We investigated the association between lopinavir (LPV) and hydroxychloroquine (HCQ) plasma concentrations and the levels of the acute-phase inflammation marker C-reactive protein (CRP). LPV plasma concentrations in 92 patients hospitalized at our institution were prospectively collected. Lopinavir-ritonavir was administered every 12 hours, 800/200 mg on day 1 and 400/100 mg on day 2 until day 5 or 7. HCQ was given at 800 mg, followed by 400 mg after 6, 24, and 48 h. Hematological, liver, kidney, and inflammation laboratory values were analyzed on the day of drug level determination. The median age of study participants was 59 (range, 24 to 85) years, and 71% were male. The median durations from symptom onset to hospitalization and treatment initiation were 7 days (interquartile range [IQR], 4 to 10) and 8 days (IQR, 5 to 10), respectively. The median LPV trough concentration on day 3 of treatment was 26.5 μg/ml (IQR, 18.9 to 31.5). LPV plasma concentrations positively correlated with CRP values (r = 0.37, P < 0.001) and were significantly lower when tocilizumab was preadministered. No correlation was found between HCQ concentrations and CRP values. High LPV plasma concentrations were observed in COVID-19 patients. The ratio of calculated unbound drug fraction to published SARS-CoV-2 50% effective concentrations (EC₅₀) indicated insufficient LPV concentrations in the lung. CRP values significantly correlated with LPV but not HCQ plasma concentrations, implying inhibition of cytochrome P450 3A4 (CYP3A4) metabolism by inflammation.

Effect of ageing on antiretroviral drug pharmacokinetics using clinical data combined with modelling and simulation

AIMS

The impact of ageing on antiretroviral pharmacokinetics remains uncertain, leading to missing dosing recommendations for elderly people living with human immunodeficiency virus (HIV: PLWH). The objective of this study was to investigate whether ageing leads to clinically relevant pharmacokinetic changes of antiretrovirals that would support a dose adjustment based on the age of the treated PLWH.

METHODS

Plasma concentrations for 10 first-line antiretrovirals were obtained in PLWH ≥55 years, participating in the Swiss HIV Cohort Study, and used to proof the predictive performance of our physiologically based pharmacokinetic (PBPK) model. The verified PBPK model predicted the continuous effect of ageing on HIV drug pharmacokinetics across adulthood (20-99 years). The impact of ethnicity on age-related pharmacokinetic changes between whites and other races was statistically analysed.

RESULTS

Clinically observed concentration-time profiles of all investigated antiretrovirals were generally within the 95% confidence interval of the PBPK simulations, demonstrating the predictive power of the modelling approach used. The predicted decline in drug clearance drove age-related pharmacokinetic changes of antiretrovirals, resulting in a maximal 70% [95% confidence interval: 40%, 120%] increase in antiretrovirals exposure across adulthood. Peak concentration, time to peak concentration and apparent volume of distribution were predicted to be unaltered by ageing. There was no statistically significant difference of age-related pharmacokinetic changes between studied ethnicities.

CONCLUSION

Dose adjustment for antiretrovirals based on the age of male and female PLWH is a priori not necessary in the absence of severe comorbidities considering the large safety margin of the current first-line HIV treatments.