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Model-Informed Drug Development
Published in Wei Zhang, Fangrong Yan, Feng Chen, Shein-Chung Chow, Advanced Statistics in Regulatory Critical Clinical Initiatives, 2022
In PK/PD modeling, the concept of ‘transduction’ refers to the processes that govern the transduction of target activation into the response in vivo [16]. Turnover or physiological indirect response models are widely used to account for delays in the time course of the pharmacological response relative to the time course of the drug concentration in plasma. Basically, drugs might stimulate or inhibit zero order input or first-order dissipation of drug response in a direct concentration-dependent manner.
The Precision Medicine Approach in Oncology
Published in David E. Thurston, Ilona Pysz, Chemistry and Pharmacology of Anticancer Drugs, 2021
Pharmacogenomics is the study of the influence of genetic variation on drug response by attempting to correlate single-nucleotide polymorphisms (SNPs) or gene expression with an agent’s efficacy or toxicity. The aim is to develop a rational approach to optimize drug therapy for individual patients by maximizing efficacy and minimizing side effects. The terms pharmacogenomics and pharmacogenetics are often used interchangeably and attempts to agree precise definitions have failed. However, pharmacogenetics is usually regarded as the study or clinical testing of genetic variation that gives rise to differing responses to drugs, while pharmacogenomics is the broader, whole genome, application of genomic technologies to new drug discovery and the further characterization of older drugs. In other words, pharmacogenomics is the application of pharmacogenetics, which examines single-gene interactions with drugs.
Pharmacology, Pharmacogenetics, and Pharmacoepidemiology: Three P’s of Individualized Therapy
Published in Brian Leyland-Jones, Pharmacogenetics of Breast Cancer, 2020
Current dosing of anticancer drugs is based on either a fixed quantity of the drug or on a dose that is normalized to the individual body surface area. This method makes the assumption that within a group of individuals there will be a uniform degree of systemic exposure to the drug. With studies reporting 2- to 10-fold variations in drug clearance, this assumption is clearly not valid (6). With drugs used in the field of oncology having narrow therapeutic indices, it thus becomes imperative that we understand the mechanisms behind the observed variability in drug response and toxicity when treating a patient with cancer. At the clinical level, variability in drug response can be explained by its pharmacology that describes the pharmacokinetic and pharmacodynamic profiles of the drug. Pharmacokinetics explain “what the body does to the drug” by describing the relationship between time and plasma concentrations of the drug metabolites. This relationship is affected by variables such as absorption, distribution, metabolism, and excretion of the drug. The underlying objective of pharmacodynamics is to describe “what the drug does to the body” by correlating drug concentration to drug effect (both beneficial and adverse effects). Both the pharmacokinetic and pharmacodynamic components of a drug are not independent, but represent a spectrum of continuous events starting with the ingestion of the drug and culminating in the observed clinical effect.
Machine learning, pharmacogenomics, and clinical psychiatry: predicting antidepressant response in patients with major depressive disorder
Published in Expert Review of Clinical Pharmacology, 2022
William V. Bobo, Bailey Van Ommeren, Arjun P. Athreya
Response to antidepressants is partially influenced by heritable factors [40]. Pharmacogenomics is the study of the contribution of genomics to variation in drug response phenotypes. Therefore, pharmacogenomics has been an essential discipline in the field’s attempts to identify biomarkers and associated mechanisms that are capable of distinguishing depressed patients who respond positively or poorly to treatment [41,42]. Although not all studies are in agreement [43,44], pharmacogenomic tailoring of antidepressant selection has shown promise for improving treatment outcomes for antidepressant-treated patients with MDD [45–47]. When combined with machine learning, the complex interactions between genetic variants, non-pharmacogenomic biomarkers, clinical measures, and sociodemographic characteristics may be identified (learned) and, if validated, may be exploited for purposes of response prediction in real-world practice [32].
Role of TRPV1 channels on glycogen synthase kinase-3β and oxidative stress in ouabain-induced bipolar disease
Published in Journal of Receptors and Signal Transduction, 2022
Osman Kukula, Mustafa Nusret Çiçekli, Sinan Şafak, Caner Günaydın
Bipolar disorder is a debilitating mental disorder marked by aberrant mood swings, affecting 1–3% population worldwide [1]. BD is characterized by manic behaviors such as highly elevated humor, irritability, poor judgments, risky behavior, decreased need for sleep, or depression [2]. Although it significantly decreases the life quality of patients, underlying mechanisms remain elusive. Increased release and reduced reuptake of neurotransmitters, which is thought to be responsible for manic and depressive episodes seen in bipolar patients, is reported to be regulated by the reduction in Na+/K+ ATPase pump activity. Currently, lithium is accepted as the gold standard and the only drug approved by the Food and Drug Administration (FDA) to treat bipolar disorder [3]. Furthermore, there are additional drugs used to treat BD, such as antidepressants, anticonvulsants, etc. [4]. Nevertheless, inadequate drug response, tolerance, and treatment resistance imply the urgent need for novel treatment options or possible targets.
Mouse models for mesothelioma drug discovery and development
Published in Expert Opinion on Drug Discovery, 2021
Kenneth P. Seastedt, Nathanael Pruett, Chuong D. Hoang
In general, while humans and mice share virtually the same set of genes, it is not always true that a drug targeting a mouse gene would exert an identical effect on the same gene in humans. The function of genes in different organisms (i.e., human versus mouse) may differ and be utilized in physiologic processes in entirely disparate ways, which would confound the interpretation of drug effects [80]. Specifically, regulation of p53 target genes diverged along species-specific pathways with dramatically different DNA binding landscapes between human and mouse [81]. Another category of differential drug response is in the spatio-temporal location of proteins impacting drug metabolism and pharmacodynamics. A well-documented example is that of fialuridine that worked against hepatitis B in mice but was toxic to humans because the protein transporter of the drug was also located in human mitochondria and not in mouse leading to human-specific mitochondria poisoning [82]. The genes of mouse and human for this drug transporter differed by three base pairs resulting in proteins with a radically different location and function. Lastly, in human diseases like cancer, the genetic landscape(s) are intrinsically complex and depend on the orchestration of gene pathways to drive biologic behavior, some of which are known and many of which remain obscure in MPM [7]. Incomplete knowledge of the entire genetic profile underlying the MPM tumor phenotype explains why the GEM mice, for example, do not manifest mesothelioma that genuinely mimics human cancer.