Application of PBPK Modeling and Simulation for Regulatory Decision Making and Its Impact on US Prescribing Information: An Update on the 2018-2019 Submissions to the US FDA's Office of Clinical Pharmacology.

Since 2016, results from physiologically based pharmacokinetic (PBPK) analyses have been routinely found in the clinical pharmacology section of regulatory applications submitted to the US Food and Drug Administration (FDA). In 2018, the Food and Drug Administration's Office of Clinical Pharmacology published a commentary summarizing the application of PBPK modeling in the submissions it received between 2008 and 2017 and its impact on prescribing information. In this commentary, we provide an update on the application of PBPK modeling in submissions received between 2018 and 2019 and highlight a few notable examples.

Principios de estadística Bayesiana y su relación con la farmacocinética aplicada / Principles of Bayesian statistics and its relationship with applied pharmacokinetics

La metodología estadística Bayesiana permite, si se conoce la probabilidad poblacional de que un suceso ocurra, modificar su valor cuando se dispone de nueva información individual. Aunque las metodologías Bayesiana y frecuentista (clásica) tienen idénticos campos de aplicación, la primera se aplica cada vez más en investigación científica y análisis de big data. En la farmacoterapia moderna, la farmacocinética clínica ha sido responsable de la expansión de la monitorización, facilitada por desarrollos técnico-analíticos y matemático-estadísticos. La farmacocinética poblacional ha permitido identificar y cuantificar las características fisiopatológicas y de tratamiento en una población de pacientes determinada, en particular en pediatría y neonatología, y otros grupos vulnerables, explicando la variabilidad farmacocinética interindividual. Asimismo, la estimación Bayesiana resulta importante como herramienta estadística aplicada en programas informáticos de optimización farmacoterapéutica cuando la monitorización farmacológica se basa en la interpretación farmacocinética clínica. Aunque con ventajas y limitaciones, la optimización farmacoterapéutica basada en la estimación Bayesiana es cada vez más usada en la actualidad, siendo el método de referencia. Esto es particularmente conveniente para la práctica clínica de rutina debido al limitado número de muestras requeridas por parte del paciente, y a la flexibilidad en cuanto a los tiempos de muestreo de sangre para cuantificación de fármacos. Así, la aplicación de los principios Bayesianos a la práctica de la farmacocinética clínica resulta en la mejora de la atención farmacoterapéutica.

If one knows the probability of an event occurring in a population, Bayesian statistics allows mo difying its value when there is new individual information available. Although the Bayesian and frequentist (classical) methodologies have identical fields of application, the first one is increasin gly applied in scientific research and big data analysis. In modern pharmacotherapy, clinical phar macokinetics has been used for the expansion of monitoring, facilitated by technical-analytical and mathematical-statistical developments. Population pharmacokinetics has allowed the identification and quantification of pathophysiological and treatment characteristics in a specific patient popu lation, especially in the pediatric and neonatal population and other vulnerable groups, explaining interindividual variability. Likewise, Bayesian estimation is important as a statistical tool applied in pharmacotherapy optimization software when pharmacological monitoring is based on clinical phar macokinetic interpretation. With its advantages and despite its limitations, pharmacotherapeutic op timization based on Bayesian estimation is increasingly used, becoming the reference method today. This characteristic is particularly convenient for routine clinical practice due to the limited number of samples required from the patient and the flexibility it shows regarding blood sampling times for drug quantification. Therefore, the application of Bayesian principles to the practice of clinical phar macokinetics has led to the improvement of pharmacotherapeutic care.

TOP: A deep mixture representation learning method for boosting molecular toxicity prediction.

At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a corresponding deep learning-based framework called TOP (the abbreviation of TOxicity Prediction). TOP integrates specifically designed data preprocessing methods, an RNN based on bidirectional gated recurrent unit (BiGRU), and fully connected neural networks for end-to-end molecular representation learning and chemical toxicity prediction. TOP can automatically learn a mixed molecular representation from not only SMILES contextual information that describes the molecule structure, but also physiochemical properties. Therefore, TOP can overcome the drawbacks of existing methods that use either of them, thus greatly promotes toxicity prediction accuracy. We conducted extensive experiments over 14 classic toxicity prediction tasks on three different benchmark datasets, including balanced and imbalanced ones. The results show that, with the help of the novel molecular representation method, TOP significantly outperforms not only three baseline machine learning methods, but also five state-of-the-art methods.

Assessing parameter uncertainty in small-n pharmacometric analyses: value of the log-likelihood profiling-based sampling importance resampling (LLP-SIR) technique.

Assessing parameter uncertainty is a crucial step in pharmacometric workflows. Small datasets with ten or fewer subjects appear regularly in drug development and therapeutic use, but it is unclear which method to assess parameter uncertainty is preferable in such situations. The aim of this study was to (i) systematically evaluate the performance of standard error (SE), bootstrap (BS), log-likelihood profiling (LLP), Bayesian approaches (BAY) and sampling importance resampling (SIR) to assess parameter uncertainty in small datasets and (ii) to evaluate methods to provide proposal distributions for the SIR. A simulation study was conducted and the 0-95% confidence interval (CI) and coverage for each parameter was evaluated and compared to reference CIs derived by stochastic simulation and estimation (SSE). A newly proposed LLP-SIR, combining the proposal distribution provided by LLP with SIR, was included in addition to conventional SE-SIR and BS-SIR. Additionally, the methods were applied to a clinical dataset. The determined CIs differed substantially across the methods. The CIs of SE, BS, LLP and BAY were not in line with the reference in datasets with ≤ 10 subjects. The best alignment was found for the LLP-SIR, which also provided the best coverage results among the SIR methods. The best overall results regarding the coverage were provided by LLP and BAY across all parameters and dataset sizes. To conclude, the popular SE and BS methods are not suitable to derive parameter uncertainty in small datasets containing ≤ 10 subjects, while best performances were observed with LLP, BAY and LLP-SIR.

An Introduction to Machine Learning.

In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever-increasing amount of data and computational power as well as the discovery of improved learning algorithms. However, the idea of a computer learning some abstract concept from data and applying them to yet unseen situations is not new and has been around at least since the 1950s. Many of these basic principles are very familiar to the pharmacometrics and clinical pharmacology community. In this paper, we want to introduce the foundational ideas of ML to this community such that readers obtain the essential tools they need to understand publications on the topic. Although we will not go into the very details and theoretical background, we aim to point readers to relevant literature and put applications of ML in molecular biology as well as the fields of pharmacometrics and clinical pharmacology into perspective.

Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis.

Clinical pharmacology is a multidisciplinary data sciences field that utilizes mathematical and statistical methods to generate maximal knowledge from data. Pharmacometrics (PMX) is a well-recognized tool to characterize disease progression, pharmacokinetics, and risk factors. Because the amount of data produced keeps growing with increasing pace, the computational effort necessary for PMX models is also increasing. Additionally, computationally efficient methods, such as machine learning (ML) are becoming increasingly important in medicine. However, ML is currently not an integrated part of PMX, for various reasons. The goals of this article are to (i) provide an introduction to ML classification methods, (ii) provide examples for a ML classification analysis to identify covariates based on specific research questions, (iii) examine a clinically relevant example to investigate possible relationships of ML and PMX, and (iv) present a summary of ML and PMX tasks to develop clinical decision support tools.

Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune-Oncology Therapies: A Tutorial for Clinical Pharmacologists.

Application of contemporary molecular biology techniques to clinical samples in oncology resulted in the accumulation of unprecedented experimental data. These "omics" data are mined for discovery of therapeutic target combinations and diagnostic biomarkers. It is less appreciated that omics resources could also revolutionize development of the mechanistic models informing clinical pharmacology quantitative decisions about dose amount, timing, and sequence. We discuss the integration of omics data to inform mechanistic models supporting drug development in immuno-oncology. To illustrate our arguments, we present a minimal clinical model of the Cancer Immunity Cycle (CIC), calibrated for non-small cell lung carcinoma using tumor microenvironment composition inferred from transcriptomics of clinical samples. We review omics data resources, which can be integrated to parameterize mechanistic models of the CIC. We propose that virtual trial simulations with clinical Quantitative Systems Pharmacology platforms informed by omics data will be making increasing impact in the development of cancer immunotherapies.

[Principles of Bayesian statistics and its relationship with applied pharmacokinetics]. / Principios de estadística Bayesiana y su relación con la farmacocinética aplicada.

If one knows the probability of an event occurring in a population, Bayesian statistics allows mo difying its value when there is new individual information available. Although the Bayesian and frequentist (classical) methodologies have identical fields of application, the first one is increasin gly applied in scientific research and big data analysis. In modern pharmacotherapy, clinical phar macokinetics has been used for the expansion of monitoring, facilitated by technical-analytical and mathematical-statistical developments. Population pharmacokinetics has allowed the identification and quantification of pathophysiological and treatment characteristics in a specific patient popu lation, especially in the pediatric and neonatal population and other vulnerable groups, explaining interindividual variability. Likewise, Bayesian estimation is important as a statistical tool applied in pharmacotherapy optimization software when pharmacological monitoring is based on clinical phar macokinetic interpretation. With its advantages and despite its limitations, pharmacotherapeutic op timization based on Bayesian estimation is increasingly used, becoming the reference method today. This characteristic is particularly convenient for routine clinical practice due to the limited number of samples required from the patient and the flexibility it shows regarding blood sampling times for drug quantification. Therefore, the application of Bayesian principles to the practice of clinical phar macokinetics has led to the improvement of pharmacotherapeutic care.

Toward Progress in Quantitative Translational Medicine: A Call to Action.

Quantitative translational medicine (QTM) is envisioned as a multifaceted discipline that will galvanize the path from idea to medicine through quantitative translation across the discovery, development, regulatory, and utilization spectrum. Here, we summarize results of an American Society for Clinical Pharmacology and Therapeutics (ASCPT) survey on barriers relevant to the advancement of QTM and propose opportunities for its deployment. Importantly, we offer a call to action to break down these barriers through patient-centered stewardship, effective communication, cross-sector collaboration, and a modernized educational curriculum.

Pharmacometrics and Systems Pharmacology 2030.

In 2012, a new journal was launched from the ASCPT family, CPT: Pharmacometrics and Systems Pharmacology (PSP) as both quantitative system pharmacology (QSP) and pharmacometrics were growing fields in pharmacology, drug development, and drug use. In this Perspective, the present editors and associate editors of PSP want to share their strategic vision of where these two fields, separately and together, should, would, or could be 10 years from now.

Statistical reporting of clinical pharmacology research.

Research in clinical pharmacology covers a wide range of experiments, trials and investigations: clinical trials, systematic reviews and meta-analyses of drug usage after market approval, the investigation of pharmacokinetic-pharmacodynamic relationships, the search for mechanisms of action or for potential signals for efficacy and safety using biomarkers. Often these investigations are exploratory in nature, which has implications for the way the data should be analysed and presented. Here we summarize some of the statistical issues that are of particular importance in clinical pharmacology research.

Essential competencies in prescribing: A first european cross-sectional study among 895 final-year medical students.

European medical students should have acquired adequate prescribing competencies before graduation, but it is not known whether this is the case. In this international multicenter study, we evaluated the essential knowledge, skills, and attitudes in clinical pharmacology and therapeutics (CPT) of final-year medical students across Europe. In a cross-sectional design, 26 medical schools from 17 European countries were asked to administer a standardized assessment and questionnaire to 50 final-year students. Although there were differences between schools, our results show an overall lack of essential prescribing competencies among final-year students in Europe. Students had a poor knowledge of drug interactions and contraindications, and chose inappropriate therapies for common diseases or made prescribing errors. Our results suggest that undergraduate teaching in CPT is inadequate in many European schools, leading to incompetent prescribers and potentially unsafe patient care. A European core curriculum with clear learning outcomes and assessments should be urgently developed.

A Quantitative Method for Weight Selection in SGDDP.

Ethnic factors pose major challenge to evaluating the treatment effect of a new drug in a targeted ethnic (TE) population in emerging regions based on the results from a multiregional clinical trial (MRCT). To address this issue with statistical rigor, Huang et al. (2012) proposed a new design of a simultaneous global drug development program (SGDDP) which used weighted Z tests to combine the information collected from the nontargeted ethnic (NTE) group in the MRCT with that from the TE group in both the MRCT and a simultaneously designed local clinical trial (LCT). An important and open question in the SGDDP design was how to downweight the information collected from the NTE population to reflect the potential impact of ethnic factors and ensure that the effect size for TE patients is clinically meaningful. In this paper, we will relate the weight selection for the SGDDP to Method 1 proposed in the Japanese regulatory guidance published by the Ministry of Health, Labour and Welfare (MHLW) in 2007. Method 1 is only applicable when true effect sizes are assumed to be equal for both TE and NTE groups. We modified the Method 1 formula for more general scenarios, and use it to develop a quantitative method of weight selection for the design of the SGDDP which, at the same time, also provides sufficient power to descriptively check the consistency of the effect size for TE patients to a clinically meaningful magnitude.

Statistical Properties of the Design for Simultaneous Global Drug Development Program.

Recently, a design was proposed for the Simultaneous Global Drug Development Program (SGDDP) to assess the impact of ethnic factors on the effect of a new treatment for a targeted ethnic (TE) population. It used weighted Z tests to combine the information collected from the TE and non-TE (NTE) subgroups in the SGDDP based on the fundamental assumption on their shared biological commonality. In this article, we mathematically formulated this assumption as the quantitative interaction between treatment effect and subgroup. We used it to more rigorously describe the hypotheses, and showed the unbiasedness of the weighted Z test. Moreover, to study the loss of efficiency from down weighting the NTE information in this SGDDP design, we compared the power of their test with that of the uniformly most powerful (UMP) test, which we showed was also a weighted Z test. We discussed that the choice of weight should balance the maximization of power when the assumption holds and the minimization of bias otherwise.

Clinical pharmacology = disease progression + drug action.

Clinical pharmacology is concerned with understanding how to use medicines to treat disease. Pharmacokinetics and pharmacodynamics have provided powerful methodologies for describing the time course of concentration and effect in individuals and in populations. This population approach may also be applied to describing the progression of disease and the action of drugs to change disease progress. Quantitative models for symptomatic and disease-modifying effects of drugs are valuable not only for describing drugs and diseases but also for identifying criteria to distinguish between types of drug actions, with implications for regulatory decisions and long-term patient care.

Robust ridge regression estimators for nonlinear models with applications to high throughput screening assay data.

Nonlinear regression is often used to evaluate the toxicity of a chemical or a drug by fitting data from a dose-response study. Toxicologists and pharmacologists may draw a conclusion about whether a chemical is toxic by testing the significance of the estimated parameters. However, sometimes the null hypothesis cannot be rejected even though the fit is quite good. One possible reason for such cases is that the estimated standard errors of the parameter estimates are extremely large. In this paper, we propose robust ridge regression estimation procedures for nonlinear models to solve this problem. The asymptotic properties of the proposed estimators are investigated; in particular, their mean squared errors are derived. The performances of the proposed estimators are compared with several standard estimators using simulation studies. The proposed methodology is also illustrated using high throughput screening assay data obtained from the National Toxicology Program.