Precision dosing in clinical medicine: present and future

Precision Dosing in Presence of Multiobjective Therapies by Integrating Reinforcement Learning and PK-PD Models: Application to Givinostat Treatment of Polycythemia Vera

Precision dosing aims to optimize and customize pharmacological treatment at the individual level. The integration of pharmacometric models with Reinforcement Learning (RL) algorithms is currently under investigation to support the personalization of adaptive dosing therapies. In this study, this hybrid technique is applied to the real multiobjective precision dosing problem of givinostat treatment in polycythemia vera (PV) patients. PV is a chronic myeloproliferative disease with an overproduction of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). The therapeutic goal is to simultaneously normalize the levels of these efficacy/safety biomarkers, thus inducing a complete hematological response (CHR). An RL algorithm, Q-Learning (QL), was integrated with a PK-PD model describing the givinostat effect on PLT, WBC, and HCT to derive both an adaptive dosing protocol (QLpop-agent) for the whole population and personalized dosing strategies by coupling a specific QL-agent to each patient (QLind-agents). QLpop-agent learned a general adaptive dosing protocol that achieved a similar CHR rate (77% vs. 83%) when compared to the actual givinostat clinical protocol on 10 simulated populations. Treatment efficacy and safety increased with a deeper dosing personalization by QLind-agents. These QL-based patient-specific adaptive dosing rules outperformed both the clinical protocol and QLpop-agent by reaching the CHR in 93% of the test patients and completely avoided severe toxicities during the whole treatment period. These results confirm that RL and PK-PD models can be valid tools for supporting adaptive dosing strategies as interesting performances were achieved in both learning a general set of rules and in customizing treatment for each patient.

Vancomycin Population Pharmacokinetic Models in Non- Critically Ill Adults Patients: a scoping review

Background

Vancomycin is an effective first-line therapy primarily in methicillin-resistant Staphylococcus aureus (MRSA) infection and Clostridium difficile, however, it has been shown that its effectiveness and the reduction of nephrotoxicity depend on maintaining adequate therapeutic levels. Population pharmacokinetic (PopPk) models attempt to parameterize the behavior of plasma concentrations in different target populations and scenarios such as renal replacement therapy, to successful therapeutic outcome and avoid these side effects.

Methods

A scoping review was conducted following the guidelines of Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR), through a search in PubMed, LILACS, OVID Medline, Scopus, Web of Science, SAGE Journals, Google Scholar and previous known registers of PopPk models in non-critically ill adult patients, published between 1998 and 2024.

Results

A total of 190 papers were fully screened, of which were included 36 studies conducted in different populations; 12 in general population, 23 in special populations (surgical, with impaired renal function, obese, elderly, with cancer and cystic fibrosis), and 1 in mixed population (general and with cancer). The main parameters in the models were renal clearance and volume of distribution. The principal covariables that affected the models were creatinine clearance and weight. All studies used internal evaluation and 4 of them used an external group.

Discussion

The technology for the development and implementation of PopPk models requires experts in clinical pharmacology and is limited to university and research centers. The software is mostly expensive and, in most cases, the pharmacokinetic models and the heterogeneity in the parameters and evaluation methods depend on which compartmental model, parameters, covariates and software have been used.

Conclusions

These models require validation in the clinical context and conducting experiments to adapt them for precision dosing in different subpopulations.

Dealing With Variable Drug Exposure Due to Variable Hepatic Metabolism: A Proof-of-Concept Application of Liquid Biopsy in Renal Impairment

Precision dosing strategies require accounting for between-patient variability in pharmacokinetics (PK), affecting drug exposure, and in pharmacodynamics (PD), affecting response achieved at the same drug concentration at the site of action. Although liquid biopsy for assessing different levels of molecular drug targets has yet to be established, individual characterization of drug elimination pathways using liquid biopsy has recently been demonstrated. The feasibility of applying this approach in conjunction with modeling tools to guide individual dosing remains unexplored. In this study, we aimed to individualize physiologically-based pharmacokinetic (PBPK) models based on liquid biopsy measurements in plasma from 25 donors with different grades of renal function who were previously administered oral midazolam as part of a microdose cocktail. Virtual twin models were constructed based on demographics, renal function, and hepatic expression of relevant pharmacokinetic pathways projected from liquid biopsy output. Simulated exposure (AUC) to midazolam was in agreement with observed data (AFE = 1.38, AAFE = 1.78). Simulated AUC variability with three dosing approaches indicated higher variability with uniform dosing (14-fold) and stratified dosing (13-fold) compared with individualized dosing informed by liquid biopsy (fivefold). Further, exosome screening revealed mRNA expression of 532 targets relevant to drug metabolism and disposition (169 enzymes and 361 transporters). Data related to these targets can be used to further individualize PBPK models for pathways relevant to PK of other drugs. This study provides additional verification of liquid biopsy-informed PBPK modeling approaches, necessary to advance strategies that seek to achieve precise dosing from the start of treatment.

Leveraging QSP Models for MIPD: A Case Study for Warfarin/INR

Warfarin dosing remains challenging due to substantial inter-individual variability, which can lead to unsafe or ineffective therapy with standard dosing. Model-informed precision dosing (MIPD) can help individualize warfarin dosing, requiring the selection of a suitable model. For models developed from clinical data, the dependence on the study design and population raises questions about generalizability. Quantitative system pharmacology (QSP) models promise better extrapolation abilities; however, their complexity and lack of validation on clinical data raise questions about applicability in MIPD. We have previously derived a mechanistic warfarin/international normalized ratio (INR) model from a blood coagulation QSP model. In this article, we evaluated the predictive performance of the warfarin/INR model in the context of MIPD using an external dataset with INR data from patients starting warfarin treatment. We assessed the accuracy and precision of model predictions, benchmarked against an empirically based reference model. Additionally, we evaluated covariate contributions and assessed the predictive performance separately in the more challenging outpatient data. The warfarin/INR model performed comparably to the reference model across various measures despite not being calibrated with warfarin initiation data. Including CYP2C9 and/or VKORC1 genotypes as covariates improved the prediction quality of the warfarin/INR model, even after assimilating 4 days of INR data. The outpatient INR exhibited higher unexplained variability, and predictions slightly exceeded observed values, suggesting that model adjustments might be necessary when transitioning from an inpatient to an outpatient setting. Overall, this research underscores the potential of QSP-derived models for MIPD, offering a complementary approach to empirical model development.

Beyond Population-Level Targets for Drug Concentrations: Precision Dosing Needs Individual-Level Targets that Include Superior Biomarkers of Drug Responses

The purpose of precision dosing is to increase the chances of therapeutic success in individual patients. This is achieved in practice by adjusting doses to reach precision dosing targets determined previously in relevant populations, ideally with robust supportive evidence showing improved clinical outcomes compared with standard dosing. But is this implicit assumption of translatable population-level precision dosing targets correct and the best for all patients? In this review, the types of precision dosing targets and how they are determined are outlined, problems with the translatability of these targets to individual patients are identified, and ways forward to address these challengers are proposed. Achieving improved clinical outcomes to support precision dosing over standard dosing is currently hampered by applying population-level targets to all patients. Just as "one-dose-fits-all" may be an inappropriate philosophy for drug treatment overall, a "one-target-fits-all" philosophy may limit the broad clinical benefits of precision dosing. Defining individual-level precision dosing targets may be needed for greatest therapeutic success. Superior future precision dosing targets will integrate several biomarkers that together account for the multiple sources of drug response variability.

Model-Informed Reinforcement Learning for Enabling Precision Dosing Via Adaptive Dosing

Precision dosing, the tailoring of drug doses to optimize therapeutic benefits and minimize risks in each patient, is essential for drugs with a narrow therapeutic window and severe adverse effects. Adaptive dosing strategies extend the precision dosing concept to time-varying treatments which require sequential dose adjustments based on evolving patient conditions. Reinforcement learning (RL) naturally fits this paradigm: it perfectly mimics the sequential decision-making process where clinicians adapt dose administration based on patient response and evolution monitoring. This paper aims to investigate the potentiality of coupling RL with population PK/PD models to develop precision dosing algorithms, reviewing the most relevant works in the field. Case studies in which PK/PD models were integrated within RL algorithms as simulation engine to predict consequences of any dosing action have been considered and discussed. They mainly concern propofol-induced anesthesia, anticoagulant therapy with warfarin and a variety of anticancer treatments differing for administered agents and/or monitored biomarkers. The resulted picture highlights a certain heterogeneity in terms of precision dosing approaches, applied methodologies, and degree of adherence to the clinical domain. In addition, a tutorial on how a precision dosing problem should be formulated in terms of the key elements composing the RL framework (i.e., system state, agent actions and reward function), and on how PK/PD models could enhance RL approaches is proposed for readers interested in delving in this field. Overall, the integration of PK/PD models into a RL-framework holds great promise for precision dosing, but further investigations and advancements are still needed to address current limitations and extend the applicability of this methodology to drugs requiring adaptive dosing strategies.

Efficacy and safety of pharmacogenomic-guided antidepressant prescribing in patients with depression: an umbrella review and updated meta-analysis

Aim

To determine the efficacy and safety of pharmacogenomics (PGx)-guided antidepressant prescribing in patients with depression through an umbrella review and updated meta-analysis.

Methods

A comprehensive systematic search was conducted on PsycINFO, PubMed, Embase and the Cochrane databases. The pooled effect sizes of randomized controlled trials (RCTs) were expressed as mean differences for continuous data and risk ratios for noncontinuous data.

Results

Patients who received PGx-guided medications were 41% to 78% more likely to achieve remission and 20% to 49% more likely to respond to antidepressants than patients receiving treatment-as-usual (TAU).

Conclusion

PGx-guided antidepressant prescribing improves the treatment of depression. However, the significance and magnitude of the benefit varies widely between studies and different PGx testing panels.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42022321324.

Integrating Omics into Functional Biomarkers of Type 1 Diabetes

Biomarkers are critical to the staging and diagnosis of type 1 diabetes (T1D). Functional biomarkers offer insights into T1D immunopathogenesis and are often revealed using "omics" approaches that integrate multiple measures to identify involved pathways and functions. Application of the omics biomarker discovery may enable personalized medicine approaches to circumvent the more recently appreciated heterogeneity of T1D progression and treatment. Use of omics to define functional biomarkers is still in its early years, yet findings to date emphasize the role of cytokine signaling and adaptive immunity in biomarkers of progression and response to therapy. Here, we share examples of the use of omics to define functional biomarkers focusing on two signatures, T-cell exhaustion and T-cell help, which have been associated with outcomes in both the natural history and treatment contexts.

Reinforcement Learning and PK-PD Models Integration to Personalize the Adaptive Dosing Protocol of Erdafitinib in Patients with Metastatic Urothelial Carcinoma

The integration of pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulations with artificial intelligence/machine learning algorithms is one of the most attractive areas of the pharmacometric research. These hybrid techniques are currently under investigation to perform several tasks, among which precision dosing. In this scenario, this paper presents and evaluates a new framework embedding PK-PD models into a reinforcement learning (RL) algorithm, Q-learning (QL), to personalize pharmacological treatment. Each patient is represented with a set of PK-PD parameters and has a personal QL agent which optimizes the individual treatment. In the training phase, leveraging PK-PD simulations, the QL agent assesses different actions, defined consistently with the clinical knowledge to consider only plausible dose-adjustments, in order to find the optimal rules. The proposed framework was evaluated to optimize the erdafitinib treatment in patients with metastatic urothelial carcinoma. This drug was approved by the US Food and Drug Administration (FDA) with a dose-adaptive protocol based on monitoring the levels of serum phosphate, which represent a biomarker of both treatment efficacy and toxicity. To evaluate the flexibility of the methodology, a heterogeneous virtual population of 141 patients was generated using an erdafitinib population PK (PopPK)-PD literature model. For each patient, treatment response was simulated by using both QL-optimized protocol and the clinical one. QL agents outperform the approved dose-adaptive rules, increasing more than 10% the efficacy and the safety of treatment at each end point. Results confirm the great potentialities of the integration of PopPK-PD models and RL algorithms to optimize precision dosing tasks.

External Validation of Obese/Critically Ill Vancomycin Population Pharmacokinetic Models in Critically Ill Patients Who Are Obese

Obesity combined with critical illness might increase the risk of acquiring infections and hence mortality. In this patient population the pharmacokinetics of antimicrobials vary significantly, making antimicrobial dosing challenging. The objective of this study was to assess the predictive performance of published population pharmacokinetic models of vancomycin in patients who are critically ill or obese for a cohort of critically ill patients who are obese. This was a multi-center retrospective study conducted at 2 hospitals. Adult patients with a body mass index of ≥30 kg/m2 were included. PubMed was searched for published population pharmacokinetic studies in patients who were critically ill or obese. External validation was performed using Monolix software. A total of 4 models were identified in patients who were obese and 5 models were identified in patients who were critically ill. In total, 138 patients who were critically ill and obese were included, and the most accurate models for these patients were the Goti and Roberts models. In our analysis, models in patients who were critically ill outperformed models in patients who were obese. When looking at the most accurate models, both the Goti and the Roberts models had patient characteristics similar to ours in terms of age and creatinine clearance. This indicates that when selecting the proper model to apply in practice, it is important to account for all relevant variables, besides obesity.

Pharmacogenomics - a minor rather than major force in clinical medicine

INTRODUCTION

Pharmacogenomics (PGx) is touted as essential for the future of precision medicine. But the opportunity cost of PGx from the prescribers' perspective is rarely considered. The aim of this article is to critique PGx-guided prescribing using clinical pharmacology principles so that important cases for PGx testing are not missed by doctors responsible for therapeutic decision making.

AREAS COVERED

Three categories of PGx and their limitations are outlined - exposure PGx, response PGx, and immune-mediated safety PGx. Clinical pharmacology reasons are given for the narrow scope of PGx-guided prescribing apart from a few medical specialties. Clinical problems for doctors that may arise from PGx are then explained, including mismatch between patients' expectations of PGx testing and the benefits or answers it provides.

EXPERT OPINION

Contrary to popular opinion, PGx is unlikely to become the cornerstone of precision medicine. Sound clinical pharmacology reasons explain why PGx-guided prescribing is unnecessary for most drugs. Pharmacogenomics is important for niche areas of prescribing but has limited clinical utility more broadly. The opportunity cost of PGx-guided prescribing is currently too great for most doctors.

Comparison of the predictive accuracy of the physiologically based pharmacokinetic (PBPK) model and population pharmacokinetic (PPK) model of vancomycin in Japanese patients with MRSA infection

INTRODUCTION

The latest therapeutic drug monitoring guidelines for vancomycin (VCM) recommend that area under the concentration-time curve is estimated based on model-informed precision dosing and used to evaluate efficacy and safety. Therefore, we predicted VCM concentrations in individual methicillin-resistant Staphylococcus aureus-infected patients using existing a physiologically based pharmacokinetic (PBPK) model and 1- and 2-compartment population pharmacokinetic (PPK) models and confirmed and verified the accuracy of the PBPK model in estimating VCM concentrations with the PPK model.

METHODS

The subjects of the study are 20 patients, and the predicted concentrations were evaluated by comparing the observed and predicted trough and peak values of VCM concentrations for individual patients.

RESULTS

The results showed good correlation between the observed and predicted trough and peak concentrations of VCM was observed generally in the PBPK model, R2 values of 0.72, 0.62, and 0.40 with trough values of 0.49, 0.40, and 0.34 with peak values for PBPK model, 1-compartment, and 2-compartment model, respectively.

CONCLUSIONS

Although the performance of the PBPK model is not as predictive as the PPK model, generally similar predictive trends were obtained, suggesting that it may be a valuable tool for rapid and accurate prediction of AUC for VCM.

Virtual twin for healthcare management

Healthcare is increasingly fragmented, resulting in escalating costs, patient dissatisfaction, and sometimes adverse clinical outcomes. Strategies to decrease healthcare fragmentation are therefore attractive from payer and patient perspectives. In this commentary, a patient-centered smart phone application called Virtual Twin for Healthcare Management (VTHM) is proposed, including its organizational layout, basic functionality, and potential clinical applications. The platform features a virtual twin hub that displays the body and its health data. This is a physiologically based human model that is "virtualized" for the patient based on their unique genetic, molecular, physiological, and disease characteristics. The spokes of the system are a full service and interoperable electronic-health record, accessible to healthcare providers with permission on any device with internet access. Theoretical case studies based on real scenarios are presented to show how VTHM could potentially improve patient care and clinical efficiency. Challenges that must be overcome to turn VTHM into reality are also briefly outlined. Notably, the VTHM platform is designed to operationalize current and future precision medicine initiatives, such as access to molecular diagnostic results, pharmacogenomics-guided prescribing, and model-informed precision dosing.

Calculation of the pharmacogenomics benefit score for patients with medication-related problems

Unexpected poor efficacy and intolerable adverse effects are medication-related problems that may result from genetic variation in genes encoding key proteins involved in pharmacokinetics or pharmacodynamics. Pharmacogenomic (PGx) testing can be used in medical practice "pre-emptively" to avoid future patient harm from medications and "reactively" to diagnose medication-related problems following their occurrence. A structured approach to PGx consulting is proposed to calculate the pharmacogenomics benefit score (PGxBS), a patient-centered objective measure of congruency between medication-related problems and patient genotypes. An example case of poor efficacy with multiple medications is presented, together with comments on the potential benefits and limitations of using the PGxBS in medical practice.

Bayesian prediction-based individualized dosing of anti-methicillin-resistant Staphylococcus aureus treatment: Recent advancements and prospects in therapeutic drug monitoring

As one of the efficient techniques for TDM, the population pharmacokinetic (popPK) model approach for dose individualization has been developed due to the rapidly growing innovative progress in computer technology and has recently been considered as a part of model-informed precision dosing (MIPD). Initial dose individualization and measurement followed by maximum a posteriori (MAP)-Bayesian prediction using a popPK model are the most classical and widely used approach among a class of MIPD strategies. MAP-Bayesian prediction offers the possibility of dose optimization based on measurement even before reaching a pharmacokinetically steady state, such as in an emergency, especially for infectious diseases requiring urgent antimicrobial treatment. As the pharmacokinetic processes in critically ill patients are affected and highly variable due to pathophysiological disturbances, the advantages offered by the popPK model approach make it highly recommended and required for effective and appropriate antimicrobial treatment. In this review, we focus on novel insights and beneficial aspects of the popPK model approach, especially in the treatment of infectious diseases with anti-methicillin-resistant Staphylococcus aureus agents represented by vancomycin, and discuss the recent advancements and prospects in TDM practice.

An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science

A physiologically-based pharmacokinetic (PBPK) model represents the structural components of the body with physiologically relevant compartments connected via blood flow rates described by mathematical equations to determine drug disposition. PBPK models are used in the pharmaceutical sector for drug development, precision medicine, and the chemical industry to predict safe levels of exposure during the registration of chemical substances. However, one area of application where PBPK models have been scarcely used is forensic science. In this review, we give an overview of PBPK models successfully developed for several illicit drugs and environmental chemicals that could be applied for forensic interpretation, highlighting the gaps, uncertainties, and limitations.

Sex Differences in Pharmacokinetics

Because women have been excluded from most clinical trials, assessment of sex differences in pharmacokinetics is available for a minority of currently prescribed drugs. In a 2020 analysis, substantial pharmacokinetic (PK) sex differences were established for 86 drugs: women given the same drug dose as men routinely generated higher blood concentrations and longer drug elimination times than men. 96% of drugs with higher PK values in women were associated with a higher incidence of adverse drug reactions (ADRs) in women than men; in the small number of instances when PKs of men exceeded those of women, this sex difference positively predicted male-biased ADRs in only 29% of cases. The absence of sex-stratified PK information for many medications raises the concern that sex differences in pharmacokinetics may be widespread and of clinical significance, contributing to sex-specific patterns of ADRs. Administering equal drug doses to women and men neglects sex differences in pharmacokinetics and body weight, risks overmedication of women, and contributes to female-biased ADRs. Evidence-based dosing adjustments are recommended to counteract this sex bias.

Sex differences in allometry for phenotypic traits in mice indicate that females are not scaled males

Sex differences in the lifetime risk and expression of disease are well-known. Preclinical research targeted at improving treatment, increasing health span, and reducing the financial burden of health care, has mostly been conducted on male animals and cells. The extent to which sex differences in phenotypic traits are explained by sex differences in body weight remains unclear. We quantify sex differences in the allometric relationship between trait value and body weight for 363 phenotypic traits in male and female mice, recorded in >2 million measurements from the International Mouse Phenotyping Consortium. We find sex differences in allometric parameters (slope, intercept, residual SD) are common (73% traits). Body weight differences do not explain all sex differences in trait values but scaling by weight may be useful for some traits. Our results show sex differences in phenotypic traits are trait-specific, promoting case-specific approaches to drug dosage scaled by body weight in mice.

Delineating gene-environment effects using virtual twins of patients treated with clozapine

Studies that focus on individual covariates, while ignoring their interactions, may not be adequate for model-informed precision dosing (MIPD) in any given patient. Genetic variations that influence protein synthesis should be studied in conjunction with environmental covariates, such as cigarette smoking. The aim of this study was to build virtual twins (VTs) of real patients receiving clozapine with interacting covariates related to genetics and environment and to delineate the impact of interacting covariates on predicted clozapine plasma concentrations. Clozapine-treated patients with schizophrenia (N = 42) with observed clozapine plasma concentrations, demographic, environmental, and genotype data were used to construct VTs in Simcyp. The effect of increased covariate virtualization was assessed by performing simulations under three conditions: "low" (demographic), "medium" (demographic and environmental interaction), and "high" (demographic and environmental/genotype interaction) covariate virtualization. Increasing covariate virtualization with interaction improved the coefficient of variation (R² ) from 0.07 in the low model to 0.391 and 0.368 in the medium and high models, respectively. Whereas R² was similar between the medium and high models, the high covariate virtualization model had improved accuracy, with systematic bias of predicted clozapine plasma concentration improving from -138.48 ng/ml to -74.65 ng/ml. A high level of covariate virtualization (demographic, environmental, and genotype) may be required for MIPD using VTs.

Pharmacogenomics in psychiatry - the challenge of cytochrome P450 enzyme phenoconversion and solutions to assist precision dosing

Pharmacogenomic (PGx) testing of cytochrome P450 (CYP) enzymes may improve the efficacy and/or safety of some medications. This is facilitated by increased availability and affordability of genotyping, the development of clinical practice PGx guidelines and regulatory support. However, the common occurrence of CYP phenoconversion, a mismatch between genotype-predicted CYP phenotype and the actual CYP phenotype, currently limits the application of PGx testing for precision dosing in psychiatry. This review proposes a stepwise approach to assist precision dosing in psychiatry via the introduction of PGx stewardship programs and innovative PGx education strategies. A future perspective on delivering precision dosing for psychiatrists is discussed that involves innovative clinical decision support systems powered by model-informed precision dosing.

Simultaneous detection of a panel of nine sedatives and metabolites in plasma from critically ill pediatric patients via UPLC-MS/MS

Sedative use can result in adverse drug reactions. Intensive care unit patients are especially at risk and pharmacokinetic modeling of drug concentrations is an approach to develop precision dosing strategies. However, limited blood sampling availability in critically ill children and need for multiple assays to quantify a variety of commonly used sedatives creates logistical challenges. The goal of this project was to develop a sensitive and selective assay for the simultaneous quantification of a panel of sedatives comprised of midazolam (MDZ), alpha hydroxymidazolam (1- OH MDZ), dexmedetomidine (DEX), morphine (MOR), morphine-3-glucuronide (M3G), morphine-6-glucuronide (M6G), fentanyl (FEN), norfentanyl (NF), and hydromorphone (HM) in small volume pediatric plasma samples. A sensitive and efficient ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS/MS) method was developed following FDA guidance for bioanalytical validation. Minimal sample preparation consisting of simple protein precipitation extraction using acetonitrile with internal standards was utilized. Analyte separation was achieved using a gradient mixture of (A: 0.15% formic acid in water and B: Acetonitrile) and a Waters Acquity C18, 1.7 µm (2.1 × 100 mm) column. Assays were linear over the clinical concentration ranges: MDZ, MOR, HM: 0.5-125 ng/mL; 1-OH MDZ, M3G, M6G: 5-500 ng/mL; and DEX, FEN, NF: 0.05-7.5 ng/mL (R2 > 0.99 for all). Assay run time was 10 min and required only 100 μL of plasma. Initial testing of samples from pediatric patients demonstrates adequacy of assay to measure sedatives and metabolites at clinical concentrations confidently in low volumes of plasma. This novel highly-sensitive and specific method to measure a total of nine different analytes (five sedatives, four metabolites) simultaneously enables comprehensive analysis of a panel of sedatives in small volumes such as in pediatric ICU patients.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Liquid Biopsy for Patient Characterization in Cardiovascular Disease: Verification against Markers of Cytochrome P450 and P-Glycoprotein Activities

Precision dosing strategies require accounting for between-patient variability in pharmacokinetics together with subsequent pharmacodynamic differences. Liquid biopsy is a valuable new approach to diagnose disease prior to the appearance of clinical signs and symptoms, potentially circumventing invasive tissue biopsies. However, the possibility of quantitative grading of biomarkers, as opposed to simply confirming their presence or absence, is relatively new. In this study, we aimed to verify expression measurements of cytochrome P450 (CYP) enzymes and the transporter P-glycoprotein (P-gp) in liquid biopsy against genotype and activity phenotype (assessed by the Geneva cocktail approach) in 30 acutely ill patients with cardiovascular disease in a hospital setting. After accounting for exosomal shedding, expression in liquid biopsy correlated with activity phenotype for CYP1A2, CYP2B6, CYP2C9, CYP3A, and P-gp (r = 0.44-0.70, P ≤ 0.05). Although genotype offered a degree of stratification, large variability (coefficient of variation (CV)) in activity (up to 157%) and expression in liquid biopsy (up to 117%) was observed within each genotype, indicating a mismatch between genotype and phenotype. Further, exosome screening revealed expression of 497 targets relevant to drug metabolism and disposition (159 enzymes and 336 transporters), as well as 20 molecular drug targets. Although there were no functional data available to correlate against these large-scale measurements, assessment of disease perturbation from healthy baseline was possible. Verification of liquid biopsy against activity phenotype is important to further individualize modeling approaches that aspire to achieve precision dosing from the start of drug treatment without the need for multiple rounds of dose optimization.

Optimized sampling to estimate vancomycin drug exposure: Comparison of pharmacometric and equation-based approaches in a simulation-estimation study

Vancomycin dosing should be accompanied by area under the concentration‐time curve (AUC)–guided dosing using model‐informed precision dosing software according to the latest guidelines. Although a peak plus a trough sample is considered the gold standard to determine the AUC, single‐sample strategies might be more economic. Yet, optimal sampling times for AUC determination of vancomycin have not been systematically evaluated. In the present study, automated one‐ or two‐sample strategies were systematically explored to estimate the AUC with a model averaging and a model selection algorithm. Both were compared with a conventional equation‐based approach in a simulation‐estimation study mimicking a heterogenous patient population (n = 6000). The optimal single‐sample timepoints were identified between 2–6.5 h post dose, with varying bias values between −2.9% and 1.0% and an imprecision of 23.3%–24.0% across the population pharmacokinetic approaches. Adding a second sample between 4.5–6.0 h improved the predictive performance (−1.7% to 0.0% bias, 17.6%–18.6% imprecision), although the difference in the two‐sampling strategies were minor. The equation‐based approach was always positively biased and hence inferior to the population pharmacokinetic approaches. In conclusion, the approaches always preferred samples to be drawn early in the profile (<6.5 h), whereas sampling of trough concentrations resulted in a higher imprecision. Furthermore, optimal sampling during the early treatment phase could already give sufficient time to individualize the second dose, which is likely unfeasible using trough sampling.

Recent advancements in clearance monitoring of monoclonal antibodies in patients with inflammatory bowel diseases

INTRODUCTION

Less than 50% of patients with inflammatory bowel diseases (IBD) receiving monoclonal antibody (mAb) therapy achieve endoscopic remission. Poor outcomes may indicate a need for dose optimization. During therapeutic drug monitoring (TDM), drug concentrations are measured, and when found too low, dosage regimen escalations are performed. To date, benefits of TDM of mAbs in patients with IBD are uncertain.

AREAS COVERED

This review presents an overview of what clearance monitoring is, how it can be performed, and why and when it may be valuable in treating patients with IBD. Virtual patients were used for illustration. A literature search was performed to summarize current evidence for clearance monitoring in IBD and other disease settings.

EXPERT OPINION

During clearance monitoring, mAb clearance is calculated and monitored over time. Higher mAb clearance in patients with IBD has been associated with higher target load (target-mediated drug disposition), protein-losing enteropathy (fecal drug loss), and immunogenicity. Although not prospectively confirmed, clearance monitoring might facilitate identification of (yet) asymptomatic disease flares or presence of (yet) undetectable anti-drug antibodies. Furthermore, clearance monitoring may be used to predict treatment outcomes. Whether dosage regimen adjustments can modify the clearance time course and the treatment outcome is to be determined.

Gender Variations in Pharmacokinetics of Paracetamol in Hausa/Fulani Ethnic group in Northwest Nigeria - A Two-stage Approach

Background:

Paracetamol is one of the most commonly used drugs worldwide and has been linked to drug-related liver damage, even when taken at recommended doses. Ingesting the upper limit of recommended doses of the drug produced a doubling of mortality when compared to not taking the drug. Acetaminophen ingestion has been implicated in the development of angioedema, the exasperation of asthma, and urticaria in patients with aspirin intolerance.

Aim:

This study aimed at assessing gender variations in the pharmacokinetics of paracetamol in Hausa/Fulani, the most populous ethnic group in Nigeria and determines a possibility of toxicity in the group.

Methods:

It was an exploratory study involving twenty participants selected by criterion sampling who satisfied inclusion criteria. They were fasted 11-h preceding acetaminophen administration to 3 h after administration. A single dose of acetaminophen, 1 g orally with 300 ml of distilled water, was administered at 8 A. M. Blood was obtained before the administration and 15, 30, and 45 min, and 1, 2, 3, 4, 5, and 6 h after the administration. Acetaminophen plasma concentrations were determined by validated reverse-phase high-performance liquid chromatography Food and Drug Administration guidelines.

Results:

Six out of 19 (31.6%) participants have higher than maximum therapeutic plasma concentration (>20 μg/ml). Pharmacokinetics parameters were higher in males except for clearance and volume of distribution.

Conclusion:

Clearance from the plasma tends to be more for females than their male counterparts. A good proportion of Hausa/Fulani is prone to acetaminophen toxicity at a therapeutic dose.

A continued learning approach for model-informed precision dosing: updating models in clinical practice

Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to include also altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, since only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step towards building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.

Dose Verification Errors in Hospitals: Literature Review of the eMAR-based Systems Used by Nurses

BACKGROUND

The effectiveness of the dose verification features of the electronic medication administration record (eMAR) and complementary systems in the hospital setting is not well understood.

PURPOSE

The authors completed a narrative synthesis of literature findings on the effectiveness of eMAR-based systems in the hospital setting.

METHODS

A literature review was carried out across 5 bibliographic databases to evaluate the safety features of current eMAR-based systems in preventing dosing errors and design issues that impede their usability.

RESULTS

While eMAR-based systems are beneficial to reducing order and drug cross-checking errors, safe dose verification features are sporadically available for targeted tasks. Overall, the eMAR had little impact on preventing low to moderate dosing errors. Dosing errors may occur because of error-prone activities that result from system design and work process issues during medication administration.

Nanoparticulate Drug Delivery Strategies to Address Intestinal Cytochrome P450 CYP3A4 Metabolism towards Personalized Medicine

Drug dosing in clinical practice, which determines optimal efficacy, toxicity or ineffectiveness, is critical to patients' outcomes. However, many orally administered therapeutic drugs are susceptible to biotransformation by a group of important oxidative enzymes, known as cytochrome P450s (CYPs). In particular, CYP3A4 is a low specificity isoenzyme of the CYPs family, which contributes to the metabolism of approximately 50% of all marketed drugs. Induction or inhibition of CYP3A4 activity results in the varied oral bioavailability and unwanted drug-drug, drug-food, and drug-herb interactions. This review explores the need for addressing intestinal CYP3A4 metabolism and investigates the opportunities to incorporate lipid-based oral drug delivery to enable precise dosing. A variety of lipid- and lipid-polymer hybrid-nanoparticles are highlighted to improve drug bioavailability. These drug carriers are designed to target different intestinal regions, including (1) local saturation or inhibition of CYP3A4 activity at duodenum and proximal jejunum; (2) CYP3A4 bypass via lymphatic absorption; (3) pH-responsive drug release or vitamin-B12 targeted cellular uptake in the distal intestine. Exploitation of lipidic nanosystems not only revives drugs removed from clinical practice due to serious drug-drug interactions, but also provide alternative approaches to reduce pharmacokinetic variability.

Constructing and analysing dynamic models with modelbase v1.2.3: a software update

BACKGROUND

Computational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies, and disease evolution and transmission. Unfortunately, despite community efforts leading to the development of SBML and the BioModels database, many published models have not been fully exploited, largely due to a lack of proper documentation or the dependence on proprietary software. To facilitate the reuse and further development of systems biology and systems medicine models, an open-source toolbox that makes the overall process of model construction more consistent, understandable, transparent, and reproducible is desired.

RESULTS AND DISCUSSION

We provide an update on the development of modelbase, a free, expandable Python package for constructing and analysing ordinary differential equation-based mathematical models of dynamic systems. It provides intuitive and unified methods to construct and solve these systems. Significantly expanded visualisation methods allow for convenient analysis of the structural and dynamic properties of models. After specifying reaction stoichiometries and rate equations modelbase can automatically assemble the associated system of differential equations. A newly provided library of common kinetic rate laws reduces the repetitiveness of the computer programming code. modelbase is also fully compatible with SBML. Previous versions provided functions for the automatic construction of networks for isotope labelling studies. Now, using user-provided label maps, modelbase v1.2.3 streamlines the expansion of classic models to their isotope-specific versions. Finally, the library of previously published models implemented in modelbase is growing continuously. Ranging from photosynthesis to tumour cell growth to viral infection evolution, all these models are now available in a transparent, reusable and unified format through modelbase.

CONCLUSION

With this new Python software package, which is written in currently one of the most popular programming languages, the user can develop new models and actively profit from the work of others. modelbase enables reproducing and replicating models in a consistent, tractable and expandable manner. Moreover, the expansion of models to their isotopic label-specific versions enables simulating label propagation, thus providing quantitative information regarding network topology and metabolic fluxes.

Customizable drug tablets with constant release profiles via 3D printing technology

Medicine should ideally be personalized as each individual has his/her own unique biological, physical, and medical dispositions. Medicine can be personalized by customizing drug tablets with the specific drug dosages, release durations, and combinations of multiple drugs. This study presents a method for fabricating drug tablets with customizable dosages, durations, and combinations of multiple drugs by using the 3D printing technology. The method focuses on fabricating customizable drug tablets with a very simple structure for delivering the constant release profile due to its importance in treatment (i.e., the drug may produce side effects if too much is released andmay not have therapeutic value is too little is released). The method is simple: it involves first printing a template using the 3D printer and fabricating the drug tablet via the template. The tablets are customized by varying the amount of excipient used, the height of the tablet, and the numberand amount of drugs used. Three different common drugs (i.e., paracetamol, phenylephrine HCl and diphenhydramine HCl) and FDA-approved excipients are studied. The simplicity of the structure of the tablet and method via templating allows the fabrication of these fully customizable drug tablets to be easily performed, low-cost, efficient, and safe for consumption. These features enable the customizable tablets to be made widely accessible to the public; hence, the concept of personalized medicine can be realized.

PK-DB: pharmacokinetics database for individualized and stratified computational modeling

A multitude of pharmacokinetics studies have been published. However, due to the lack of an open database, pharmacokinetics data, as well as the corresponding meta-information, have been difficult to access. We present PK-DB (https://pk-db.com), an open database for pharmacokinetics information from clinical trials. PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, bodyweight, smoking status, genetic variants); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) pharmacokinetic parameters (e.g. clearance, half-life, area under the curve) and (iv) measured pharmacokinetic time-courses. Key features are the representation of experimental errors, the normalization of measurement units, annotation of information to biological ontologies, calculation of pharmacokinetic parameters from concentration-time profiles, a workflow for collaborative data curation, strong validation rules on the data, computational access via a REST API as well as human access via a web interface. PK-DB enables meta-analysis based on data from multiple studies and data integration with computational models. A special focus lies on meta-data relevant for individualized and stratified computational modeling with methods like physiologically based pharmacokinetic (PBPK), pharmacokinetic/pharmacodynamic (PK/PD), or population pharmacokinetic (pop PK) modeling.

Concept and utility of population pharmacokinetic and pharmacokinetic/pharmacodynamic models in drug development and clinical practice

Due to frequent clinical trial failures and consequently fewer new drug approvals, the need for improvement in drug development has, to a certain extent, been met using model-based drug development. Pharmacometrics is a part of pharmacology that quantifies drug behaviour, treatment response and disease progression based on different models (pharmacokinetic - PK, pharmacodynamic - PD, PK/PD models, etc.) and simulations. Regulatory bodies (European Medicines Agency, Food and Drug Administration) encourage the use of modelling and simulations to facilitate decision-making throughout all drug development phases. Moreover, the identification of factors that contribute to variability provides a basis for dose individualisation in routine clinical practice. This review summarises current knowledge regarding the application of pharmacometrics in drug development and clinical practice with emphasis on the population modelling approach.

Liquid Biopsy Enables Quantification of the Abundance and Interindividual Variability of Hepatic Enzymes and Transporters

Variability in individual capacity for hepatic elimination of therapeutic drugs is well recognized and is associated with variable expression and activity of liver enzymes and transporters. Although genotyping offers some degree of stratification, there is often large variability within the same genotype. Direct measurement of protein expression is impractical due to limited access to tissue biopsies. Hence, determination of variability in hepatic drug metabolism and disposition using liquid biopsy (blood samples) is an attractive proposition during drug development and in clinical practice. This study used a multi-"omic" strategy to establish a liquid biopsy technology intended to assess hepatic capacity for metabolism and disposition in individual patients. Plasma exosomal analysis (n = 29) revealed expression of 533 pharmacologically relevant genes at the RNA level, with 147 genes showing evidence of expression at the protein level in matching liver tissue. Correction of exosomal RNA expression using a novel shedding factor improved correlation against liver protein expression for 97 liver-enriched genes. Strong correlation was demonstrated for 12 key drug-metabolizing enzymes and 4 drug transporters. The developed test allowed reliable patient stratification, and in silico trials demonstrated utility in adjusting drug dose to achieve similar drug exposure between patients with variable hepatic elimination. Accordingly, this approach can be applied in characterization of volunteers prior to enrollment in clinical trials and for patient stratification in clinical practice to achieve more precise individual dosing.

A Systematic Assessment of US Food and Drug Administration Dosing Recommendations For Drug Development Programs Amenable to Response-Guided Titration

A key goal in drug development is optimized dosing for patients. Interactions between drug developers and regulatory scientists throughout development are important for the optimization of dosing and serve as a forum to discuss approaches for optimal dosing, such as precision or individualized dosing. To date, there has not been a systematic assessment of the advice provided by the US Food and Drug Administration (FDA) to drug developers from an individualized dosing perspective. Here, we reviewed FDA recommendations on dose selection for efficacy trials at end-of-phase meetings between the FDA and drug developers for 76 new molecular entities approved between 2013 and 2017 that are considered amenable for an individualized dosing method, response-guided titration. Forty FDA dosing recommendations were identified as specific to dose selection and design of the respective efficacy trials and subsequently: (i) characterized based on if they were supportive of individualized dosing and (ii) compared with dosing regimens used in efficacy trials and labeling at approval to evaluate if FDA recommendations were implemented. Of these 40 recommendations for efficacy trials, 35 (88%) were considered supportive of individualized dosing. Eighteen of these 40 recommendations (45%) were incorporated into efficacy trials and 11 (28%) were incorporated into labeling. This research suggests that early FDA-sponsor interactions can support the study of doses in efficacy trials that may lead to individualized dosing strategies in labeling.

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.

Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy

Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health economics, and drug regulations.

Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia

Drug development, from preclinical to clinical studies, is a lengthy and complex process. There is an increased interest in the Kingdom of Saudi Arabia (KSA) to promote innovation, research and local content including clinical trials (Phase I-IV). Currently, there are over 650 registered clinical trials in Saudi Arabia, and this number is expected to increase. An important part of drug development and clinical trials is to assure the safe and effective use of drugs. Clinical pharmacology plays a vital role in informed decision making during the drug development stage as it focuses on the effects of drugs in humans. Disciplines such as pharmacokinetics, pharmacodynamics and pharmacogenomics are components of clinical pharmacology. It is a growing discipline with a range of applications in all phases of drug development, including selecting optimal doses for Phase I, II and III studies, evaluating bioequivalence and biosimilar studies and designing clinical studies. Incorporating clinical pharmacology in research as well as in the requirements of regulatory agencies will improve the drug development process and accelerate the pipeline. Clinical pharmacology is also applied in direct patient care with the goal of personalizing treatment. Tools such as therapeutic drug monitoring, pharmacogenomics and model informed precision dosing are used to optimize dosing for patients at an individual level. In KSA, the science of clinical pharmacology is underutilized and we believe it is important to raise awareness and educate the scientific community and healthcare professionals in terms of its applications and potential. In this review paper, we provide an overview on the use and applications of clinical pharmacology in both drug development and clinical care.

Artificial Intelligence and Machine Learning Applied at the Point of Care

Introduction

The increasing availability of healthcare data and rapid development of big data analytic methods has opened new avenues for use of Artificial Intelligence (AI)- and Machine Learning (ML)-based technology in medical practice. However, applications at the point of care are still scarce.

Objective

Review and discuss case studies to understand current capabilities for applying AI/ML in the healthcare setting, and regulatory requirements in the US, Europe and China.

Methods

A targeted narrative literature review of AI/ML based digital tools was performed. Scientific publications (identified in PubMed) and grey literature (identified on the websites of regulatory agencies) were reviewed and analyzed.

Results

From the regulatory perspective, AI/ML-based solutions can be considered medical devices (i.e., Software as Medical Device, SaMD). A case series of SaMD is presented. First, tools for monitoring and remote management of chronic diseases are presented. Second, imaging applications for diagnostic support are discussed. Finally, clinical decision support tools to facilitate the choice of treatment and precision dosing are reviewed. While tested and validated algorithms for precision dosing exist, their implementation at the point of care is limited, and their regulatory and commercialization pathway is not clear. Regulatory requirements depend on the level of risk associated with the use of the device in medical practice, and can be classified into administrative (manufacturing and quality control), software-related (design, specification, hazard analysis, architecture, traceability, software risk analysis, cybersecurity, etc.), clinical evidence (including patient perspectives in some cases), non-clinical evidence (dosing validation and biocompatibility/toxicology) and other, such as e.g. benefit-to-risk determination, risk assessment and mitigation. There generally is an alignment between the US and Europe. China additionally requires that the clinical evidence is applicable to the Chinese population and recommends that a third-party central laboratory evaluates the clinical trial results.

Conclusions

The number of promising AI/ML-based technologies is increasing, but few have been implemented widely at the point of care. The need for external validation, implementation logistics, and data exchange and privacy remain the main obstacles.

Sex differences in pharmacokinetics predict adverse drug reactions in women

BACKGROUND

Women experience adverse drug reactions, ADRs, nearly twice as often as men, yet the role of sex as a biological factor in the generation of ADRs is poorly understood. Most drugs currently in use were approved based on clinical trials conducted on men, so women may be overmedicated. We determined whether sex differences in drug pharmacokinetics, PKs, predict sex differences in ADRs.

METHODS

Searches of the ISI Web of Science and PubMed databases were conducted with combinations of the terms: drugs, sex or gender, pharmacokinetics, pharmacodynamics, drug safety, drug dose, and adverse drug reaction, which yielded over 5000 articles with considerable overlap. We obtained information from each relevant article on significant sex differences in PK measures, predominantly area under the curve, peak/maximum concentrations, and clearance/elimination rates. ADRs were identified from every relevant article and recorded categorically as female-biased, male-biased, or not sex-biased.

RESULTS

For most of the FDA-approved drugs examined, elevated blood concentrations and longer elimination times were manifested by women, and these PKs were strongly linked to sex differences in ADRs. Of the 86 drugs evaluated, 76 had higher PK values in women; for 59 drugs with clinically identifiable ADRs, sex-biased PKs predicted the direction of sex-biased ADRs in 88% of cases. Ninety-six percent of drugs with female-biased PK values were associated with a higher incidence of ADRs in women than men, but only 29% of male-biased PKs predicted male-biased ADRs. Accessible PK information is available for only a small fraction of all drugs CONCLUSIONS: Sex differences in pharmacokinetics strongly predict sex-specific ADRs for women but not men. This sex difference was not explained by sex differences in body weight. The absence of sex-stratified PK information in public records for hundreds of drugs raises the concern that sex differences in PK values are widespread and of clinical significance. The common practice of prescribing equal drug doses to women and men neglects sex differences in pharmacokinetics and dimorphisms in body weight, risks overmedication of women, and contributes to female-biased adverse drug reactions. We recommend evidence-based dose reductions for women to counteract this sex bias.

Software Tools for Model-Informed Precision Dosing: How Well Do They Satisfy the Needs?

Model-informed precision dosing (MIPD) software tools are used to optimize dosage regimens in individual patients, aiming to achieve drug exposure targets associated with desirable clinical outcomes. Over the last few decades, numerous MIPD software tools have been developed. However, they have still not been widely integrated into clinical practice. This study focuses on identifying the requirements for and evaluating the performance of the currently available MIPD software tools. First, a total of 22 experts in the field of precision dosing completed a web survey to assess the importance (from 0; do not agree at all, to 10; completely agree) of 103 pre-established software tool criteria organized in eight categories: user-friendliness and utilization, user support, computational aspects, population models, quality and validation, output generation, privacy and data security, and cost. Category mean ± pooled standard deviation importance scores ranged from 7.2 ± 2.1 (user-friendliness and utilization) to 8.5 ± 1.8 (privacy and data security). The relative importance score of each criterion within a category was used as a weighting factor in the subsequent evaluation of the software tools. Ten software tools were identified through literature and internet searches: four software tools were provided by companies (DoseMeRx, InsightRX Nova, MwPharm++, and PrecisePK) and six were provided by non-company owners (AutoKinetics, BestDose, ID-ODS, NextDose, TDMx, and Tucuxi). All software tools performed well in all categories, although there were differences in terms of in-built software features, user interface design, the number of drug modules and populations, user support, quality control, and cost. Therefore, the choice for a certain software tool should be made based on these differences and personal preferences. However, there are still improvements to be made in terms of electronic health record integration, standardization of software and model validation strategies, and prospective evidence for the software tools’ clinical and cost benefits.

Precision Dosing Priority Criteria: Drug, Disease, and Patient Population Variables

The administered dose of a drug modulates whether patients will experience optimal effectiveness, toxicity including death, or no effect at all. Dosing is particularly important for diseases and/or drugs where the drug can decrease severe morbidity or prolong life. Likewise, dosing is important where the drug can cause death or severe morbidity. Since we believe there are many examples where more precise dosing could benefit patients, it is worthwhile to consider how to prioritize drug-disease targets. One key consideration is the quality of information available from which more precise dosing recommendations can be constructed. When a new more precise dosing scheme is created and differs significantly from the approved label, it is important to consider the level of proof necessary to either change the label and/or change clinical practice. The cost and effort needed to provide this proof should also be considered in prioritizing drug-disease precision dosing targets. Although precision dosing is being promoted and has great promise, it is underutilized in many drugs and disease states. Therefore, we believe it is important to consider how more precise dosing is going to be delivered to high priority patients in a timely manner. If better dosing schemes do not change clinical practice resulting in better patient outcomes, then what is the use? This review paper discusses variables to consider when prioritizing precision dosing candidates while highlighting key examples of precision dosing that have been successfully used to improve patient care.

Personalized Medicine: Recent Progress in Cancer Therapy

Translational research has revolutionized how we develop new treatments for cancer patients. The change from an organ-centric concept guiding treatment choice towards deep molecular analysis, driving a personalized approach, is one of the most important advances of modern oncology. Several tools such as next generation sequencing and RNA sequencing have greatly improved the capacity to detect predictive and prognostic molecular alterations. Detection of gene mutations, amplifications, and fusions has therefore altered the history of several diseases in both a localized and metastatic setting. This shift in perspective, in which attention is focused on the specific molecular alterations of the tumor, has opened the door to personalized treatment. This situation is reflected in the increasing number of basket trials selecting specific molecular targets. Nonetheless, some weaknesses need to be addressed. The complexity of cancer cells enriched with concomitant molecular alterations complicates identification of the driver. Moreover, tumor heterogeneity could be responsible for the lack of benefit when targeted agents are used. In light of this, there is growing interest in the role of multidisciplinary committees or molecular tumor boards to try to enhance selection. The aim of this review is to critically analyze the evolution of cancer treatment towards a precision approach, underlining some recent successes and unexpected failures.

Recent advances in taste transduction and signaling

In the last few years, single-cell profiling of taste cells and ganglion cells has advanced our understanding of transduction, encoding, and transmission of information from taste buds as relayed to the central nervous system. This review focuses on new knowledge from these molecular approaches and attempts to place this in the context of previous questions and findings in the field. The individual taste cells within a taste bud are molecularly specialized for detection of one of the primary taste qualities: salt, sour, sweet, umami, and bitter. Transduction and transmitter release mechanisms differ substantially for taste cells transducing sour (Type  III cells) compared with those transducing the qualities of sweet, umami, or bitter (Type II cells), although ultimately all transmission of taste relies on activation of purinergic P2X receptors on the afferent nerves. The ganglion cells providing innervation to the taste buds also appear divisible into functional and molecular subtypes, and each ganglion cell is primarily but not exclusively responsive to one taste quality.

Precision dosing to avoid adverse drug reactions

Adverse drug reactions (ADRs) have traditionally been managed by trial and error, adjusting drug and dose selection reactively following patient harm. With an improved understanding of ADRs, and the patient characteristics that increase susceptibility, precision medicine technologies enable a proactive approach to ADRs and support clinicians to change prescribing accordingly. This commentary revisits the famous pharmacology–toxicology continuum first postulated by Paracelsus 500 years ago and explains why precision dosing is needed to help avoid ADRs in modern clinical practice. Strategies on how to improve precision dosing are given, including more research to establish better precision dosing targets in the cases of greatest need, easier access to dosing instructions via e-prescribing, improved monitoring of patients with novel biomarkers of drug response, and further application of model-informed precision dosing.

Kinetic Modeling of Steady-State Situations in Cytochrome P450 Enzyme Reactions

In the course of investigations of the kinetics of individual reactions of cytochrome P450 (P450) enzymes, a number of points about the complexity of P450 enzyme kinetics have become apparent. Several of these are of particular relevance to work with P450 enzymes in the course of drug development and lead optimization, particularly with regard to estimating in vitro kinetic parameters and dealing with enzyme inhibitors. Modern simulation modeling has been applied to situations involving issues of preincubation time with moderate strength and strong inhibitors, inhibition by tightly bound ligands that have been identified in P450 enzymes, extensive substrate depletion, P450 reactions with a rate-limiting step after product formation, and the consumption of an inhibitor during a reaction by either a P450 enzyme being monitored or another one in a mixture. The results all follow from first principles, and simulations reveal the extent of their significance in various settings. The order of addition of substrate and inhibitors can change the apparent outcome (inhibition constant, K i), and the effect of the order is more pronounced with a stronger inhibitor. Substrate depletion alters parameters (Michaelis constant, K m) and can generate apparently sigmoidal plots. A rate-limiting step after product formation lowers the apparent K m and distorts K i Consumption of an inhibitor during a reaction affects K i and differs depending on which enzyme is involved. The results are relevant with P450 enzymes and other enzymes as well. SIGNIFICANCE STATEMENT: Kinetic simulations have been used to address several potential problems in enzyme kinetic analysis. Although the simulations done here are general for enzyme reactions, several problems addressed here are particularly relevant to cytochrome P450 reactions encountered in drug development work.

Precision medicine technology hype or reality? The example of computer-guided dosing

Novel technologies labelled as ‘precision medicine’ are targeting all aspects of clinical care. Whilst some technological advances are undeniably exciting, many doctors at the frontline of healthcare view precision medicine as being out of reach for their patients. Computer-guided dosing is a precision medicine technology that predicts drug concentrations and drug responses based on individual patient characteristics. In this opinion piece, the example of computer-guided dosing is used to illustrate eight features of a precision medicine technology less likely to be hyperbole and more likely to improve patient care. Positive features in this regard include: (1) fitting the definition of ‘precision medicine’; (2) addressing a major clinical problem that negatively impacts patient care; (3) a track record of high-quality medical science published via peer-reviewed literature; (4) well-defined clinical cases for application; (5) quality evidence of benefits measured by various clinical, patient and health economic endpoints; (6) strong economic drivers; (7) user friendliness, including easy integration into clinical workflow, and (8) recognition of importance by patients and their endorsement for broader clinical use. Barriers raised by critics of the approach are given to balance the view. The value of computer-guided dosing will be decided ultimately by the extent to which it can improve cost-effective patient care.