Quantitative Clinical Pharmacology Supports the Bridging From i.v. Dosing and Approval of s.c. Rituximab in B-Cell Hematological Malignancies.

A fixed-dose subcutaneous (s.c.) formulation of the anti-CD20 antibody, rituximab, has been developed to address safety, infusion time, and patient comfort concerns relating to intravenous (i.v.) dosing, and has been approved based upon a pharmacokinetic (PK)-clinical bridging strategy, which demonstrated noninferiority of s.c. vs. i.v. dosing in malignancies, including follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL). A clinical development plan was undertaken to identify rituximab s.c. doses achieving noninferior exposure to rituximab i.v., and to confirm PK-clinical bridging, with the same efficacy and similar safety. This drew upon data from 1,579 patients with FL, CLL, or diffuse large B-cell lymphoma in 5 clinical studies, and showed minimum steady-state serum concentration (Ctrough ) as the most appropriate exposure bridging measure. Population PK models were developed, simulations were run using covariates and PK parameters from clinical studies, and exposure-efficacy and -safety analyses performed. Population PKs showed a two-compartment model with time-dependent and -independent clearances. Clearance and volume were predominantly influenced by body surface area; disposition and elimination were similar for the s.c. and i.v. formulations. After s.c. administration, patients with FL and CLL achieved noninferior exposures to i.v. dosing. Overall, rituximab exposure and route of administration did not influence clinical responses in patients with FL or CLL, and there was no association between exposure and safety events. Ctrough was shown to be an effective pharmacologic-clinical bridging parameter for rituximab in patients with FL or CLL. Clinically effective exposures are achieved with either s.c. or i.v. dosing.

There is Only One Valid Definition of Clearance: Critical Examination of Clearance Concepts Reveals the Potential for Errors in Clinical Drug Dosing Decisions.

Drug dosing decisions in clinical medicine and in introducing a drug to market for the past 60 years are based on the pharmacokinetic/clinical pharmacology concept of clearance. We used chemical reaction engineering models to demonstrate the limitations of presently employed clearance measurements based upon systemic blood concentration in reflecting organ clearance. The belief for the last 49 years that in vivo clearance is independent of the mechanistic model for organ clearance is incorrect. There is only one valid definition of clearance. Defining organ clearance solely on the basis of systemic blood concentrations can lead to drug dosing errors when drug effect sites reside either in an eliminating organ exhibiting incremental clearance or in a non-eliminating organ where intraorgan concentration is governed by transporter actions. Attempts to predict clearance are presently hampered by the lack of recognition that what we are trying to predict is a well-stirred model clearance.

Clinical Pharmacology Regulatory Sciences in Drug Development and Precision Medicine: Current Status and Emerging Trends.

In the regulatory setting, clinical pharmacology focuses on the impact of intrinsic and extrinsic factors on inter-patient and intra-subject variability in drug exposure and response. This translational science contributes to the understanding of the benefit-risk profile in individual patients and the development of relevant therapeutic monitoring and management strategies. Clinical pharmacology also plays a major role in the development and qualification of drug development tools. This article presented some recent examples to illustrate the important roles of clinical pharmacology in drug development and evaluation. In addition, emerging trends in clinical pharmacology regulatory sciences were also discussed, including the Model-Informed Drug Development (MIDD) pilot program, the use of real-world data to generate real-world evidence, and leveraging advances in basic, biomedical, and clinical science into useful tools for drug development and evaluation. Continued advances in clinical pharmacology can be the basis of more rational and efficient drug development and improved access to new drug treatments that are tailored to the patient to achieve better efficacy and safety.

EurOP2E - the European Open Platform for Prescribing Education, a consensus study among clinical pharmacology and therapeutics teachers.

PURPOSE: Sharing and developing digital educational resources and open educational resources has been proposed as a way to harmonize and improve clinical pharmacology and therapeutics (CPT) education in European medical schools. Previous research, however, has shown that there are barriers to the adoption and implementation of open educational resources. The aim of this study was to determine perceived opportunities and barriers to the use and creation of open educational resources among European CPT teachers and possible solutions for these barriers. METHODS: CPT teachers of British and EU medical schools completed an online survey. Opportunities and challenges were identified by thematic analyses and subsequently discussed in an international consensus meeting. RESULTS: Data from 99 CPT teachers from 95 medical schools were analysed. Thirty teachers (30.3%) shared or collaboratively produced digital educational resources. All teachers foresaw opportunities in the more active use of open educational resources, including improving the quality of their teaching. The challenges reported were language barriers, local differences, lack of time, technological issues, difficulties with quality management, and copyright restrictions. Practical solutions for these challenges were discussed and include a peer review system, clear indexing, and use of copyright licenses that permit adaptation of resources. CONCLUSION: Key challenges to making greater use of CPT open educational resources are a limited applicability of such resources due to language and local differences and quality concerns. These challenges may be resolved by relatively simple measures, such as allowing adaptation and translation of resources and a peer review system.

Evaluating Patients With Impaired Renal Function During Drug Development: Highlights From the 2019 US FDA Pharmaceutical Science and Clinical Pharmacology Advisory Committee Meeting.

Patients with multiple chronic conditions, including more advanced chronic kidney disease (CKD), are often excluded from clinical trials, creating challenges in deriving appropriate dosing information and labeling. This article summarizes the May 7, 2019, US Food and Drug Administration Pharmaceutical Science and Clinical Pharmacology Advisory Committee Meeting, which solicited expert opinions on how to enroll patients with more advanced CKD into clinical trials as well as the assumptions behind and different approaches of exposure-matching.

The scientific basis of rational prescribing. A guide to precision clinical pharmacology based on the WHO 6-step method.

BACKGROUND AND METHODS: This opinion paper expanded on the WHO "six-step approach to optimal pharmacotherapy," by detailed exploration of the underlying pharmacological and pathophysiological principles. This exercise led to the identification of a large number of domains of research that should be addressed to make clinical pharmacology progress toward "precision clinical pharmacology," as a prerequisite for precision medicine. RESULT: In order to improve clinical efficacy and safety in patient groups (to guide drug development) as well as in individuals (to guide therapeutic options and optimize clinical outcome), developments in clinical pharmacology should at least tackle the following: (1) molecular diagnostic assays to guide drug design and development and allow physicians to identify the optimal targets for therapy in the individual patient in a quick and precise manner (to guide selection of the right drug for the right patient); (2) the setting up and validation of biomarkers of target engagement and modification as predictors of clinical efficacy and safety; (3) integration of physiological PK/PD models and intermediate markers of pharmacological effects with the natural evolution of the disease to predict the drug dose that most effectively improves clinical outcome in patient groups and individuals, making use of advanced modeling technologies (building on deterministic models, machine-learning, and deep learning algorithms); (4) methodology to validate human or humanized in vitro, ex vivo, and in vivo models for their ability to predict clinical outcome with investigational therapies, including nucleic acids or recombinant genes together with vectors (including viruses or nanoparticles), cell therapy, or therapeutic vaccines; (5) methodological complements to the gold-standard, large Phase 3 randomized clinical trial to provide clinically relevant and reliable data on the efficacy and safety of all treatment options at the population level (pragmatic clinical trials), as well as in small groups of patients (as low as n = 1); (6) regulatory science, so as to optimize the ethical review process, documentation, and monitoring of clinical trials, improve efficiency, and reduce costs of clinical drug development; (7) interventions to effectively improve patient compliance and to rationalize polypharmacy for the reduction of adverse effects and the enhancement of therapeutic interactions; and (8) appraisal of the ecological and societal impact of drug use to safeguard against environmental hazards (following the "One Health" concept) and to reduce drug resistance. DISCUSSION AND CONCLUSION: As can be seen, precision clinical pharmacology aims at being highly translational, which will require very large panels of complementary skills. Interdisciplinary collaborations, including non-clinical pharmacologists, will be key to achieve such an ambitious program.

Prior information for population pharmacokinetic and pharmacokinetic/pharmacodynamic analysis: overview and guidance with a focus on the NONMEM PRIOR subroutine.

Population pharmacokinetic analysis is used to estimate pharmacokinetic parameters and their variability from concentration data. Due to data sparseness issues, available datasets often do not allow the estimation of all parameters of the suitable model. The PRIOR subroutine in NONMEM supports the estimation of some or all parameters with values from previous models, as an alternative to fixing them or adding data to the dataset. From a literature review, the best practices were compiled to provide a practical guidance for the use of the PRIOR subroutine in NONMEM. Thirty-three articles reported the use of the PRIOR subroutine in NONMEM, mostly in special populations. This approach allowed fast, stable and satisfying modelling. The guidance provides general advice on how to select the most appropriate reference model when there are several previous models available, and to implement and weight the selected parameter values in the PRIOR function. On the model built with PRIOR, the similarity of estimates with the ones of the reference model and the sensitivity of the model to the PRIOR values should be checked. Covariates could be implemented a priori (from the reference model) or a posteriori, only on parameters estimated without prior (search for new covariates).

Challenges in Drug Development Posed by the COVID-19 Pandemic: An Opportunity for Clinical Pharmacology.

The unprecedented challenges posed by the coronavirus disease 2019 (COVID-19) pandemic highlight the urgency for applying clinical pharmacology and model-informed drug development in (i) dosage optimization for COVID-19 therapies, (ii) approaching therapeutic dilemmas in clinical trial settings, and (iii) maximizing value of information from impacted non-COVID-19 trials. More than ever, we have a responsibility for adaptive evidence synthesis with a Totality of Evidence mindset in this race against time across biomedical research, clinical practice, drug development, and regulation.

Medidas y recomendaciones en los estudios de farmacología clínica durante la pandemia COVID-19 / Measures and recommendations in clinical pharmacology studies during the COVID-19 pandemic

En el marco de la actual situación de emergencia sanitaria, la Administración Nacional de Medicamentos, Alimentos y Tecnología Médica (ANMAT), establece las siguientes medidas y recomendaciones con el objeto de preservar las actividades de los estudios de farmacología clínica (EFC) protegiendo la seguridad y bienestar de los participantes del estudio. Los patrocinadores de los EFC deberán confeccionar un plan de mitigación de riesgo para extremar las medidas tendientes a evitar el contagio y diseminación de COVID-19 así como la saturación del sistema sanitario del país. Este plan deberá quedar debidamente documentado en el archivo de cada estudio y será notificado a los investigadores, centros de investigación, comités de Ética y a la ANMAT. Su aplicación no requiere aprobación previa como modificación sustancial por esta Administración. Algunos estudios podrán requerir modificaciones particulares que no se encuentren reflejadas en el plan de mitigación. Estas deberán ser consensuadas con el equipo de investigación y plasmarse en un documento. Este plan deberá ser presentado por cada patrocinador por única vez ante la ANMAT en un trámite caratulado como "comunicación a DERM" por la plataforma de trámites a distancia (TAD) y esta Administración Nacional tomará conocimiento del mismo. No será necesario esperar a una respuesta de esta Administración para su implementación.

Model-Informed Artificial Intelligence: Reinforcement Learning for Precision Dosing.

The availability of multidimensional data together with the development of modern techniques for data analysis represent an exceptional opportunity for clinical pharmacology. Data science-defined in this special issue as the novel approaches to the collection, aggregation, and analysis of data-can significantly contribute to characterize drug-response variability at the individual level, thus enabling clinical pharmacology to become a critical contributor to personalized healthcare through precision dosing. We propose a minireview of methodologies for achieving precision dosing with a focus on an artificial intelligence technique called reinforcement learning, which is currently used for individualizing dosing regimen in patients with life-threatening diseases. We highlight the interplay of such techniques with conventional pharmacokinetic/pharmacodynamic approaches and discuss applicability in drug research and early development.

WHO guide to good prescribing is 25 years old: quo vadis?

INTRODUCTION: Twenty-five years ago, the World Health Organization (WHO) published the Guide to Good Prescribing (GGP), followed by the accompanying Teacher's Guide to Good Prescribing (TGGP). The GGP is based on a normative 6-step model for therapeutic reasoning and prescribing, and provides a six-step guide for students to the process of rational prescribing. METHOD: We reviewed the need to update both WHO publications by evaluating their use and impact, including new (theoretical) insights and demands. Based on information from literature, Internet, and other (personal) sources, we draw the following conclusions. RESULTS: 1. An update of the GGP and TGGP, both in terms of content and form, is necessary because of the current need for these tools (irrational medicine use and unavailability of medicines), the lack of similar documents, and the lack of connection with recent developments, such as Internet and modern education; 2. The basic (6-step) model of the GGP is effective in terms of rational prescribing in the undergraduate situation and is still consistent with current theories about (context) learning, clinical decision-making, and clinical practice; 3. The dissemination and introduction of the GGP and TGGP in education has been successful so far, but is still not optimal because of lack of support and cooperation. CONCLUSIONS: On the basis of the evaluation results, a plan for the revision of the GGP and TGGP is presented.

Validation of the Spartan RXCYP2C19 Genotyping Assay Utilizing Blood Samples.

The antiplatelet agent clopidogrel, a prodrug that requires bioactivation through the cytochrome P450 2C19 (CYP2C19) enzyme, is commonly prescribed post-percutaneous coronary intervention (PCI). Genetic variation in CYP2C19 contributes to individual variability in clopidogrel response, and can lead to adverse cardiovascular events. Incorporating CYP2C19 testing during routine clinical care helps identify high-risk patients, and provides the opportunity for pharmacotherapeutic interventions in the early post-PCI period. The Spartan RX CYP2C19 System has emerged as an optimal genotyping assay for use in clinical care due to ease of use, utilization of buccal swabs, and rapid turnaround time. However, workflow constraints related to sample collection and processing, storage, time, and personnel were encountered when integrating testing into clinical care. To improve clinical workflow and successfully implement CYP2C19 genotyping at our institution, we validated the Spartan RX System to return genotype utilizing blood samples. Our Molecular Diagnostic Laboratory tested 26 known reference materials and both blood and buccal swab samples from 23 patients and volunteers using the Spartan RX Assay. Genotype results were 100% concordant between DNA from blood and buccal swabs for all patients or volunteers, and consistent with expected results for the 26 reference materials. For reproducibility, three samples were tested in at least four separate runs, with all resulting genotypes in agreement between runs. Post-validation, the laboratory began offering CYP2C19 testing during clinical care. DNA extracted from blood can serve as a genomic DNA source for the Spartan RX Assay. Alteration of the methodology allowed for clinical implementation to support genotype-guided therapy.