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Current Opinion in Chemical Biology Jun 2019Pharmacokinetic/pharmacodynamic (PK/PD) models predict the effect time course resulting from a drug dose. In this review, we summarize the development of mechanistic... (Review)
Review
Pharmacokinetic/pharmacodynamic (PK/PD) models predict the effect time course resulting from a drug dose. In this review, we summarize the development of mechanistic PK/PD models that explicitly integrate the kinetics of drug-target interactions into predictions of drug activity. Such mechanistic models are expected to have several advantages over approaches in which concentration and effect are linked using variations of the Hill equation, and where preclinical data are often used as a starting point for modeling drug activity. Instead, explicit use of the full kinetic scheme for drug binding enables time-dependent changes in target occupancy to be calculated using the kinetics of drug-target interactions and drug PK, providing a more precise picture of target engagement and drug action in the non-equilibrium environment of the human body. The mechanistic PK/PD models also generate target vulnerability functions that link target occupancy and effect, and inform on the sensitivity of a target to engagement by a drug. Key factors such as the rate of target turnover can also be integrated into the modeling which, together with target vulnerability, provide additional information on the PK profile required to achieve the desired pharmacological effect and on the utility of kinetic selectivity in developing drugs for specific targets.
Topics: Binding Sites; Humans; Models, Theoretical; Pharmacokinetics; Pharmacology
PubMed: 31030171
DOI: 10.1016/j.cbpa.2019.03.008 -
Clinical Pharmacology and Therapeutics Feb 2016Biomarkers have the potential to expedite drug development, increase patient safety, and optimize clinical response. Yet few have achieved regulatory qualification. A... (Review)
Review
Biomarkers have the potential to expedite drug development, increase patient safety, and optimize clinical response. Yet few have achieved regulatory qualification. A survey was conducted to clarify industry's perspective on biomarker qualification and identify the most promising biomarkers for drug development. The results across toxicities/clinical areas highlight challenges in regulatory qualification, although early prioritization and alignment on an evidentiary standard framework are key factors in facilitating biomarker development and qualification.
Topics: Biomarkers; Biomarkers, Pharmacological; Drug Industry; Health Care Sector; Humans; Patient Safety; Pharmaceutical Preparations; Pharmacology; Surveys and Questionnaires; United States; United States Food and Drug Administration
PubMed: 26378777
DOI: 10.1002/cpt.264 -
International Journal of Molecular... Mar 2023Life is chiral, as its constituents consist, to a large degree, of optically active molecules, be they macromolecules (proteins, nucleic acids) or small biomolecules.... (Review)
Review
Life is chiral, as its constituents consist, to a large degree, of optically active molecules, be they macromolecules (proteins, nucleic acids) or small biomolecules. Hence, these molecules interact disparately with different enantiomers of chiral compounds, creating a preference for a particular enantiomer. This chiral discrimination is of special importance in medicinal chemistry, since many pharmacologically active compounds are used as racemates-equimolar mixtures of two enantiomers. Each of these enantiomers may express different behaviour in terms of pharmacodynamics, pharmacokinetics, and toxicity. The application of only one enantiomer may improve the bioactivity of a drug, as well as reduce the incidence and intensity of adverse effects. This is of special significance regarding the structure of natural products since the great majority of these compounds contain one or several chiral centres. In the present survey, we discuss the impact of chirality on anticancer chemotherapy and highlight the recent developments in this area. Particular attention has been given to synthetic derivatives of drugs of natural origin, as naturally occurring compounds constitute a major pool of new pharmacological leads. Studies have been selected which report the differential activity of the enantiomers or the activities of a single enantiomer and the racemate.
Topics: Humans; Chemistry, Pharmaceutical; Drug-Related Side Effects and Adverse Reactions; Stereoisomerism
PubMed: 36982753
DOI: 10.3390/ijms24065679 -
British Journal of Clinical Pharmacology Jun 2017Research in clinical pharmacology covers a wide range of experiments, trials and investigations: clinical trials, systematic reviews and meta-analyses of drug usage...
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.
Topics: Data Interpretation, Statistical; Humans; Models, Statistical; Pharmacology, Clinical; Research; Sample Size
PubMed: 28321897
DOI: 10.1111/bcp.13254 -
Science Translational Medicine Mar 2012The emerging discipline of systems pharmacology aims to combine analysis and computational modeling of cellular regulatory networks with quantitative pharmacology... (Review)
Review
The emerging discipline of systems pharmacology aims to combine analysis and computational modeling of cellular regulatory networks with quantitative pharmacology approaches to drive the drug discovery processes, predict rare adverse events, and catalyze the practice of personalized precision medicine. Here, we introduce the concept of enhanced pharmacodynamic (ePD) models, which synergistically combine the desirable features of systems biology and current PD models within the framework of ordinary or partial differential equations. ePD models that analyze regulatory networks involved in drug action can account for a drug's multiple targets and for the effects of genomic, epigenomic, and posttranslational changes on the drug efficacy. This new knowledge can drive drug discovery and shape precision medicine.
Topics: Decision Making; Models, Theoretical; Pharmacology; Systems Biology
PubMed: 22440734
DOI: 10.1126/scitranslmed.3003563 -
Biochemical Pharmacology Jul 2018Methadone is a synthetic, long-acting opioid with a single chiral center forming two enantiomers, (R)-methadone and (S)-methadone, each having specific pharmacological... (Review)
Review
Methadone is a synthetic, long-acting opioid with a single chiral center forming two enantiomers, (R)-methadone and (S)-methadone, each having specific pharmacological actions. Concentrations of (R)- and (S)-methadone above therapeutic levels have the ability to cause serious, life-threatening, and fatal side effects. This toxicity can be due in part to the pharmacogenetics of an individual, which influences the pharmacokinetic and pharmacodynamic properties of the drug. Methadone is primarily metabolized in the liver by cytochrome P450 (CYP) enzymes, predominately by CYP2B6, followed by CYP3A4, 2C19, 2D6, and to a lesser extent, CYP2C18, 3A7, 2C8, 2C9, 3A5, and 1A2. Single nucleotide polymorphisms (SNPs) located within CYPs have the potential to play an important role in altering methadone metabolism and pharmacodynamics. Several SNPs in the CYP2B6, 3A4, 2C19, 2D6, and 3A5 genes result in increases in methadone plasma concentrations, decreased N-demethylation, and decreased methadone clearance. In particular, carriers of CYP2B6*6/*6 may have a greater risk for detrimental adverse effects, as methadone metabolism and clearance are diminished in these individuals. CYP2B6*4, on the other hand, has been observed to decrease plasma concentrations of methadone due to increased methadone clearance. The involvement, contribution, and understanding the role of SNPs in CYP2B6, and other CYP genes, in methadone metabolism can improve the therapeutic uses of methadone in patient outcome and the development of personalized medicine.
Topics: Analgesics, Opioid; Animals; Cytochrome P-450 Enzyme System; Humans; Methadone; Opioid-Related Disorders; Pharmacogenetics; Polymorphism, Single Nucleotide
PubMed: 29458047
DOI: 10.1016/j.bcp.2018.02.020 -
Journal of Pharmacokinetics and... Feb 2022Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease... (Meta-Analysis)
Meta-Analysis Review
Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.
Topics: Drug Development; Machine Learning; Models, Biological; Network Pharmacology; Pharmacology; Systems Biology
PubMed: 34671863
DOI: 10.1007/s10928-021-09790-9 -
European Journal of Pharmaceutical... Oct 2016Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in... (Review)
Review
Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and disease models can be extended to account for internal systems interactions. It is demonstrated how SP models can be used to predict the effects of multi-target interactions and of homeostatic feedback on the pharmacological response. In addition it is shown how DS models may be used to distinguish symptomatic from disease modifying effects and to predict the long term effects on disease progression, from short term biomarker responses. It is concluded that incorporation of expressions to describe the interactions in biological network analysis opens new avenues to the understanding of the effects of drug treatment on the fundamental aspects of biological systems behavior.
Topics: Animals; Drug Discovery; Humans; Models, Biological; Pharmacology, Clinical; Systems Biology
PubMed: 27131606
DOI: 10.1016/j.ejps.2016.04.027 -
Clinical Pharmacology and Therapeutics Jan 2019Online learning, an essential component of most traditional contact-based educational programs, must be of high quality to contribute effectively to learning. The... (Review)
Review
Online learning, an essential component of most traditional contact-based educational programs, must be of high quality to contribute effectively to learning. The availability of first-class web-based materials is particularly valued by both learners and educators in resource-poor nations. In this Practice article, we introduce the International Union of Basic and Clinical Pharmacology (IUPHAR) Pharmacology Education Project (PEP) (https://www.pharmacologyeducation.org/), a freely accessible online learning resource intended to support education and training in pharmacological sciences worldwide.
Topics: Education, Distance; Humans; Internationality; Pharmacology
PubMed: 30588614
DOI: 10.1002/cpt.1278 -
Therapeutic Drug Monitoring Apr 2019Although the monitoring of drug therapies based on the determination of drug concentrations in biological materials is certainly an important instrument for...
Although the monitoring of drug therapies based on the determination of drug concentrations in biological materials is certainly an important instrument for individualized dosing and dose adjustment with a broad variety of pharmaceuticals, its role is limited by the fact that it does not reflect pharmacodynamic (PD) and toxicodynamic interactions such as those caused by individual and environment-related factors. However, these interactions are important for both the efficacy and the safety of the drug therapy. Therefore, during recent years, there is an increased interest in personalized drug therapy as reflected by the development and clinical implementation of molecular "biomarkers" that are direct or surrogate markers of pharmacological effects [PD therapeutic drug monitoring (TDM)]. Moreover, this process is driven by new developments in instrumentation, such as mass spectrometry and array technologies, and in computational biology/pharmacology, databases, and bioinformatics. This Focus Issue of the journal focuses on current achievements in and status of PD TDM with different classes of drugs. The contributions to the present issue of Therapeutic Drug Monitoring provide a critical analysis of current practices of TDM with their limitations, introduce newer promising biomarkers in the field of PD TDM, discuss the challenges faced to date in translating preclinical tools into clinical settings, and point out recent advances in the establishment of modeling approaches that apply to pharmacokinetics (PK)/PD as well as pharmacogenetic information.
Topics: Biomarkers; Drug Monitoring; Humans; Pharmacology
PubMed: 30883504
DOI: 10.1097/FTD.0000000000000627