ADME Gene-Related Pharmacogenomic Labeling of FDA-Approved Drugs: Comparison with Clinical Pharmacogenetics Implementation Consortium (CPIC) Evidence Levels.

Pharmacogenomics (PGx) can facilitate the transition to patient-specific drug regimens and thus improve their efficacy and reduce toxicity. The aim of this study was to evaluate the overlap of PGx classification for drug absorption, distribution, metabolism, and elimination (ADME)-related genes in the U.S. Food and Drug Administration (FDA) PGx labeling and in the Clinical Pharmacogenetics Implementation Consortium (CPIC) database. FDA-approved drugs and PGx labeling for ADME genes were identified in the CPIC database. Drugs were filtered by their association with ADME (pharmacokinetics)-related genes, PGx FDA labeling class, and CPIC evidence level. FDA PGx labeling was classified as either actionable, informative, testing recommended, or testing required, and varying CPIC evidence levels as either A, B, C, or D. From a total of 442 ADME and non-ADME gene-drug pairs in the CPIC database, 273, 55, and 48 pairs were excluded for lack of FDA labeling, mixed CPIC evidence level provisional classification, and non-ADME gene-drug pairs, respectively. The 66 ADME gene-drug pairs were classified into the following categories: 10 (15%) informative, 49 (74%) actionable, 6 (9%) testing recommended, and 1 (2%) testing required. CYP2D6 was the most prevalent gene among the FDA PGx labeling. From the ADME gene-drug pairs with both FDA and CPIC PGx classification, the majority of the drugs were for depression, cancer, and pain medications. The ADME gene-drug pairs with FDA PGx labeling considerably overlap with CPIC classification; however, a large number of ADME gene-drug pairs have only CPIC evidence levels but not FDA classification. PGx actionable labeling was the most common classification, with CYP2D6 as the most prevalent ADME gene in the FDA PGx labeling. Health professionals can impact therapeutic outcomes via pharmacogenetic interventions by analyzing and reconciling the FDA labels and CPIC database.

Evaluation of pharmacogenomic evidence for drugs related to ADME genes in CPIC database.

OBJECTIVES: Clinical Pharmacogenetics Implementation Consortium (CPIC) is a platform that advances the pharmacogenomics (PGx) practice by developing evidence-based guidelines. The purpose of this study was to analyze the CPIC database for ADME related genes and their corresponding drugs, and evidence level for drug-gene pairs; and to determine the presence of these drug-gene pairs in the highest mortality diseases in the United States. METHODS: CPIC database was evaluated for drug-gene pairs related to absorption, distribution, metabolism, and excretion (ADME) properties. National Vital Statistics from Centers for Disease Control and Prevention was used to identify the diseases with the highest mortality. CPIC levels are assigned to different drug-gene pairs based on varying levels of evidence as either A, B, C, or D. All drug-gene pairs assigned with A/B, B/C, or C/D mixed levels were excluded from this study. A stepwise exclusion process was followed to determine the prevalence of various ADME drug-gene pairs among phase I/II enzymes or transporters and stratify the drug-gene pairs relevant to different disease conditions most commonly responsible for death in the United States. RESULTS: From a total of 442 drug-gene pairs in the CPIC database, after exclusion of 86 drug-gene pairs with levels A/B, B/C, or C/D, and 211 non-ADME related genes, 145 ADME related drug-gene pairs resulted. From the 145 ADME related drug-genes pairs, the following were the distribution of levels: Level A: 43 (30%), Level B: 22 (15%), Level C: 59 (41%), Level D: 21 (14%). The most prevalent ADME gene with CPIC level A classification was cytochrome P450 2C9 (CYP2C9) (26%) and overall, the most prevalent ADME gene in the CPIC database was CYP2D6 (30%). The most prevalent diseases related to the CPIC evidence related drugs were cancer and depression. CONCLUSIONS: We found that there is an abundance of ADME related genes in the CPIC database, including in the high mortality disease states of cancer and depression. There is a differential level of pharmacogenomic evidence in drug-gene pairs enlisted in CPIC where levels A and D having the greatest number of drug-gene pairs. CYP2D6 was the most common ADME gene with CPIC evidence for drug-gene pairs. Pharmacogenomic applications of CPIC evidence can be leveraged to individualize patient therapy and lower adverse effect events.

ADME and Pharmacokinetic Properties of Remdesivir: Its Drug Interaction Potential.

On 11 March 2020, the World Health Organization (WHO) classified the Coronavirus Disease 2019 (COVID-19) as a global pandemic, which tested healthcare systems, administrations, and treatment ingenuity across the world. COVID-19 is caused by the novel beta coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Since the inception of the pandemic, treatment options have been either limited or ineffective. Remdesivir, a drug originally designed to be used for Ebola virus, has antiviral activity against SARS-CoV-2 and has been included in the COVID-19 treatment regimens. Remdesivir is an adenosine nucleotide analog prodrug that is metabolically activated to a nucleoside triphosphate metabolite (GS-443902). The active nucleoside triphosphate metabolite is incorporated into the SARS-CoV-2 RNA viral chains, preventing its replication. The lack of reported drug development and characterization studies with remdesivir in public domain has created a void where information on the absorption, distribution, metabolism, elimination (ADME) properties, pharmacokinetics (PK), or drug-drug interaction (DDI) is limited. By understanding these properties, clinicians can prevent subtherapeutic and supratherapeutic levels of remdesivir and thus avoid further complications in COVID-19 patients. Remdesivir is metabolized by both cytochrome P450 (CYP) and non-CYP enzymes such as carboxylesterases. In this narrative review, we have evaluated the currently available ADME, PK, and DDI information about remdesivir and have discussed the potential of DDIs between remdesivir and different COVID-19 drug regimens and agents used for comorbidities. Considering the nascent status of remdesivir in the therapeutic domain, extensive future work is needed to formulate safer COVID-19 treatment guidelines involving this medication.

Simulation of Remdesivir Pharmacokinetics and Its Drug Interactions.

PURPOSE: Remdesivir, a drug originally developed against Ebola virus, is currently recommended for patients hospitalized with coronavirus disease of 2019 (COVID-19). In spite of United States Food and Drug Administration's recent assent of remdesivir as the only approved agent for COVID-19, there is limited information available about the physicochemical, metabolism, transport, pharmacokinetic (PK), and drug-drug interaction (DDI) properties of this drug. The objective of this in silico simulation work was to simulate the biopharmaceutical and DDI behavior of remdesivir and characterize remdesivir PK properties in special populations which are highly affected by COVID-19. METHODS: The Spatial Data File format structures of remdesivir prodrug (GS-5734) and nucleoside core (GS-441524) were obtained from the PubChem database to upload into the GastroPlus software 9.8 version (Simulations Plus Inc., USA). The Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) Predictor and PKPlus modules of GastroPlus were used to simulate physicochemical and PK properties, respectively, in healthy and predisposed patients. Physiologically based pharmacokinetic (PBPK) modeling of GastroPlus was used to simulate different patient populations based on age, weight, liver function, and renal function status. Subsequently, these data were used in the Drug-Drug Interaction module to simulate drug interaction potential of remdesivir with other COVID-19 drug regimens and with agents used for comorbidities. RESULTS: Remdesivir nucleoside core (GS-441524) is more hydrophilic than the inactive prodrug (GS-5734) with nucleoside core demonstrating better water solubility. GS-5734, but not GS-441524, is predicted to be metabolized by CYP3A4. Remdesivir is bioavailable and its clearance is achieved through hepatic and renal routes. Differential effects of renal function, liver function, weight, or age were observed on the PK profile of remdesivir. DDI simulation study of remdesivir with perpetrator drugs for comorbidities indicate that carbamazepine, phenytoin, amiodarone, voriconazole, diltiazem, and verapamil have the potential for strong interactions with victim remdesivir, whereas agents used for COVID-19 treatment such as chloroquine and ritonavir can cause weak and strong interactions, respectively, with remdesivir. CONCLUSIONS: GS-5734 (inactive prodrug) appears to be a superior remdesivir derivative due to its hepatic stability, optimum hydrophilic/lipophilic balance, and disposition properties. Remdesivir disposition can potentially be affected by different physiological and pathological conditions, and by drug interactions from COVID-19 drug regimens and agents used for comorbidities.

Simulation of Physicochemical and Pharmacokinetic Properties of Vitamin D3 and Its Natural Derivatives.

Vitamin D3 is an endogenous fat-soluble secosteroid, either biosynthesized in human skin or absorbed from diet and health supplements. Multiple hydroxylation reactions in several tissues including liver and small intestine produce different forms of vitamin D3. Low serum vitamin D levels is a global problem which may origin from differential absorption following supplementation. The objective of the present study was to estimate the physicochemical properties, metabolism, transport and pharmacokinetic behavior of vitamin D3 derivatives following oral ingestion. GastroPlus software, which is an in silico mechanistically-constructed simulation tool, was used to simulate the physicochemical and pharmacokinetic behavior for twelve vitamin D3 derivatives. The Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) Predictor and PKPlus modules were employed to derive the relevant parameters from the structural features of the compounds. The majority of the vitamin D3 derivatives are lipophilic (log P values >5) with poor water solubility which are reflected in the poor predicted bioavailability. The fraction absorbed values for the vitamin D3 derivatives were low except for calcitroic acid, 1,23S,25-trihydroxy-24-oxo-vitamin D3, and (23S,25R)-1,25-dihydroxyvitamin D3-26,23-lactone each being greater than 90% fraction absorbed. Cytochrome P450 3A4 (CYP3A4) is the primary hepatic enzyme along with P-glycoprotein involved in the disposition of the vitamin D derivatives. Lipophilicity and solubility appear to be strongly associated with the oral absorption of the vitamin D3 derivatives. Understanding the ADME properties of vitamin D3 derivatives with the knowledge of pharmacological potency could influence the identification of pharmacokinetically most acceptable vitamin D3 derivative for routine supplementation.