SARS-CoV-2 Main Protease Inhibitors from Natural Product Repository as Therapeutic Candidates for the Treatment of Coronaviridae Infections.

BACKGROUND: The main protease (Mpro) is a crucial enzyme for the life cycle of SARS-CoV-2 and a validated target for the treatment of COVID-19 infection. Natural products have been a proper alternative for treating viral diseases by modulating different steps of the life cycle of many viruses. OBJECTIVE: This review article is designed to summarize the cumulative information of natural-derived Mpro inhibitors that are validated by experimental biological testing. METHODS: The natural-derived Mpro inhibitors of SARS-CoV-2 that have been discovered since the emergence of the COVID-19 pandemic are reviewed in this article. Only natural products with experimental validation are reported in this article. Collected compounds are classified according to their chemical identity into flavonoids, phenolic acids, quinones, alkaloids, chromones, stilbenes, tannins, lignans, terpenes, and other polyphenolic and miscellaneous natural-derived Mpro inhibitors. CONCLUSION: These compounds could serve as scaffolds for further lead-structure optimization for desirable potency, a larger margin of safety, and better oral activity.

Population Pharmacokinetics Modeling of Lamotrigine in Jordanian Epileptic Patients Using Dried Blood Spot Sampling.

AIMS: To characterize the population pharmacokinetics of lamotrigine in Jordanian epileptic patients and to identify factors affecting therapeutic parameters. PATIENTS AND METHODS: A population pharmacokinetics model for lamotrigine was established based on a prospectively collected data of 52 steady-state concentrations from 38 adult and pediatric patients with epilepsy. Lamotrigine concentrations were determined by a dried blood spot liquid chromatography method. Data were analyzed according to a one-compartment model with first-order absorption and elimination using the nonlinear mixed effect modeling program. The covariates effect of total body weight, gender, age, and co-medication with topiramate, carbamazepine, phenytoin, phenobarbital, and valproic acid on lamotrigine clearance were investigated using a stepwise forward addition followed by a stepwise backward elimination. RESULTS: The final population pharmacokinetics model for lamotrigine clearance was as follows: CL/Fpop=θ1*exp (θ3*age)*exp (θ5*carbamazepine)*exp (θ6*valproic acid) , where θ1 is the relative clearance (L/hr) estimated, and θ3, θ5, and θ6 are the fixed parameters relating to age and co-medication with carbamazepine and valproic acid, respectively.The population mean value of lamotrigine total clearance generated in the final model (with covariates) was 2.12 L/hr. Inter-individual variability and residual unexplained variability expressed as the coefficient of variation was 37.1 and 26.1%, respectively. CONCLUSION: Lamotrigine total clearance in the Jordanian patients is comparable to that reported by others for Caucasian patients. Age and concomitant therapy with carbamazepine and valproic acid significantly affected lamotrigine clearance, and accounted for 48% of its inter-individual variability.

Population Pharmacokinetics of Ibrutinib in Healthy Adults.

BACKGROUND AND OBJECTIVES: Ibrutinib is an antineoplastic agent that reduces B-cell proliferation by inhibiting Bruton's tyrosine kinase. We describes population pharmacokinetics of ibrutinib in healthy adults, and explores potential patient characteristics associated with ibrutinib pharmacokinetics. METHODS: A population pharmacokinetic modeling approach was applied to 39 healthy subjects. Modeling was performed using Monolix (v.2019R2). Serial blood samples to measure the plasma ibrutinib concentration were collected following the oral administration of 140 mg ibrutinib on two different occasions under fasting conditions. Demographic and clinical information were evaluated as possible predictors of ibrutinib pharmacokinetics during model development. Simulations (using mlxR: R package v.4.0.2) following the administration of therapeutic doses were performed to explore the clinical implications of identified covariates on ibrutinib steady-state concentrations. RESULTS: A two-compartment model with zero order absorption best fit the data. Inter-individual and inter-occasion variability were quantified by the proposed model. We identified smoking status as a significant covariate associated with ibrutinib clearance. Smoking was found to increase ibrutinib clearance by approximately 60%, which resulted in a reduction in simulated steady-state concentrations by around 40%. CONCLUSION: The model can be used to simulate clinical trials or various dosing scenarios. The proposed model can be used to optimize ibrutinib dosing based on the smoking status.

A time-to-event analysis describing virologic response in patients with chronic hepatitis C infection.

The objective of this project was to describe longitudinal change in chronic hepatitis C virologic reponse using time-to-event (TTE) analysis and to identify patient characteristics that determine the dynamics of this change. We compiled demographic, clinical, and genetic data from 715 chronic hepatitis C virus (HCV) patients treated with pegylated interferon (PEG-IFN) alfa-2a and ribavirin. TTE modelling described the time between antiviral treatment initiation and the first observation of undetectable HCV RNA. A lognormal TTE model was selected to describe time to first undetectable HCV RNA. The identified predictors of prolonged time to achieve undetectable HCV RNA include HCV genotype 1, low pre-treatment ALT level, older age, or with elevated baseline haemoglobin level. In conclusion, a cohort of patients with low probability of achieving SVR can be identified. This project identifies patients with a low risk of responding to PEG-IFN alfa-2a and ribavirin combination.

Bayesian Population Pharmacokinetic Modeling of Eltrombopag in Chronic Hepatitis C Patients.

BACKGROUND AND OBJECTIVES: Eltrombopag is a thrombopoietic growth factor that is approved for the treatment of thrombocytopenia in chronic hepatitis C virus (HCV) patients. We aimed to describe eltrombopag population pharmacokinetics in hepatitis C patients. Bayesian statistical approach will be applied to screen for patients' characteristics associated with eltrombopag pharmacokinetic parameters. METHODS: A population pharmacokinetic analysis was conducted using WinBUGS version 1.4.3. Data from 483 individuals with chronic HCV infection were analyzed. This analysis is a secondary analysis of two clinical studies (ENABLE1 and ENABLE2) sponsored by GlaxoSmithKline. Several patients' characteristics were examined as possible covariates of the population pharmacokinetic model. Prior information from previous studies was incorporated in the bayesian model as prior distribution to estimate pharmacokinetic parameters. RESULTS: A two-compartment pharmacokinetic model with first-order absorption with exponential error model best fit the data. We identified East Asian race and total bilirubin level as predictors of eltrombopag clearance. Typical value for distributional clearance was 0.762 L/h (95% Bayesian credible set, 0.703-0.826), for volume of distribution of the central and peripheral compartments were 12 L (10.9-13.4) and 10.9 L (10.4-11.5), and for absorption lag time was 0.947 h (0.918-0.977). Assuming an average total bilirubin of 21.7 µmol/L, the typical elimination clearance value for an East Asian patient was 0.14 L/h and for other races was 0.20 L/h. CONCLUSIONS: Eltrombopag pharmacokinetic behavior was described using population bayesian approach. This model can be applied to optimize eltrombopag dosing in order to reduce the incidence of thrombocytopenia in HCV-infected patient receiving interferon-based therapy.

A population pharmacodynamic model characterizing neutropenia associated with pegylated interferon alpha 2-a therapy in patients with chronic hepatitis C viral infection.

Neutropenia is a hematologic disorder commonly reported in patients with chronic hepatitis C viral (HCV) infection. The objective of the present analysis is to describe the change in neutrophil count resulting from peglated interferon alpha 2-a (PEG-IFN α-2a) therapy in HCV-infected patients. A population pharmacodynamic model will be developed. We also plan to identify patient characteristics that contribute to the development of PEG-IFN α-2a-induced neutropenia in hepatitis C patients. A population pharmacodynamic modeling approach was applied to a cohort of patients (n = 292) with chronic HCV infection. Modeling was performed using NONMEM 6. Data was obtained from two phases III studies sponsored by Hoffmann-La Roche. Covariate screening was applied to evaluate various demographic and clinical characteristics as possible predictors of pharmacodynamic parameter during model development. A total of 4517 neutrophil counts from 292 subjects were analyzed by the proposed population pharmacodynamic model. A constant residual error model was used to the log-transformed neutrophil count. Platelet baseline count and uric acid level were identified as predictors of neutrophil pharmacodynamic model. Increased baseline platelet count is expected to result in higher neutrophil baseline. A higher neutrophil baseline is also expected in patients with increased uric acid level. In conclusion, a mechanistic pharmacodynamic model was developed. The effect of various covariates was included in the model. This allows the prediction of neutrophil count following antiviral therapy in patients with hepatitis C infection. Clinical studies: NV15942 and NV15801.

Patient characteristics associated with peglyated interferon alfa-2a induced neutropenia in chronic hepatitis C patients.

Neutropenia is a haematologic disorder commonly reported in patients with chronic hepatitis C virus (HCV) infection treated with pegylated interferon alfa-2a (PEG-IFN α-2a). The objective of the present project is to identify patient characteristics associated with neutropenia in hepatitis C patients. Demographic, clinical, and genetic data from 715 patients with chronic HCV infection treated with PEG-IFN α-2a and ribavirin. The outcome variable was the development of grade 3 or 4 neutropenia, defined as the decrease in neutrophil counts below 1 109 /L anytime during study. Predictors of neutropenia were identified using a 2-stage approach. First, univariate analysis was performed to identify possible predictors of neutropenia. T test was used for continuous variables and Fisher's exact test was used for categorical variables. Second, multiple logistic regression with stepwise addition was then performed using predictors identified in the univariate analysis step to produce final model containing independent predictors at P < .05. Logistic regression identified female gender, absolute neutrophils counts, and cholesterol level as the main predictors of neutropenia. Female gender increases the odds of experiencing neutropenia by 86% compared to male gender. A 1 unit (mmol/L) increase in cholesterol level decreases the odds of developing neutropenia by 13%. A 55% reduction in the likelihood of developing neutropenia for a 1 unit (109 /L) increase in the absolute neutrophils counts. Patients with high risk of developing neutropenia can be identified. Identification of this cohort allows early intervention to prevent neutropenia. Possible intervention is to administer drugs that raise neutrophil count such as filgrastim before neutropenia occurs.

Evaluation of the impact of waterpipe tobacco smoke exposure on the activity and expression of rat hepatic CYP450: a pharmacokinetic study.

Waterpipe smoke contains many toxic constituents that can alter drug pharmacokinetics. This study assessed the effect of waterpipe smoke exposure on the activity and expression of CYP450 enzymes in rats. Animals (n = 10/group) were exposed to either waterpipe smoke or side-stream cigarette smoke for 1 h/day (6 days/week) for 31 days, or fresh air (control). An intragastric cocktail solution containing three probe drugs, phenacetin, chlorzoxazone and testosterone was administered to assess the activity of CYP1A2, CYP2E1 and CYP3A, respectively. Serum concentrations were determined using LC-MS/MS and the pharmacokinetic parameters were calculated. The mRNA expression of hepatic enzymes was also quantified. Waterpipe and cigarette smoke exposure did not significantly alter the pharmacokinetics of phenacetin, chlorzoxazone and testosterone. For example, the clearance and drug exposure values were comparable among groups for all probe drugs. Additionally, there was no significant effect of waterpipe and cigarette smoke on mRNA expression of hepatic CYP1A2, CYP2E1 and CYP3A2. The results demonstrate that waterpipe smoke exposure had no effect on the functional expression of three key CYP450 isoforms in rats. Future research is required with longer exposure periods to waterpipe smoke. Such work serves to enhance current understanding of effect of waterpipe smoke exposure on pharmacokinetics.

A Bayesian Approach for Population Pharmacokinetic Modeling of Pegylated Interferon α-2a in Hepatitis C Patients.

BACKGROUND: Pegylated interferon α-2a (PEG-IFN-α-2a) is an antiviral drug used for the treatment of chronic hepatitis C virus (HCV) infection. This study describes the population pharmacokinetics of PEG-IFN-α-2a in hepatitis C patients using a Bayesian approach. A possible association between patient characteristics and pharmacokinetic parameters is also explored. METHODS: A Bayesian population pharmacokinetic modeling approach, using WinBUGS version 1.4.3, was applied to a cohort of patients (n = 292) with chronic HCV infection. Data were obtained from two phase III studies sponsored by Hoffmann-La Roche. Demographic and clinical information were evaluated as possible predictors of pharmacokinetic parameters during model development. RESULTS: A one-compartment model with an additive error best fitted the data, and a total of 2271 PEG-IFN-α-2a measurements from 292 subjects were analyzed using the proposed population pharmacokinetic model. Sex was identified as a predictor of PEG-IFN-α-2a clearance, and hemoglobin baseline level was identified as a predictor of PEG-IFN-α-2a volume of distribution. CONCLUSION: A population pharmacokinetic model of PEG-IFN-α-2a in patients with chronic HCV infection was presented in this study. The proposed model can be used to optimize PEG-IFN-α-2a dosing in patients with chronic HCV infection. Optimal PEG-IFN-α-2a selection is important to maximize response and/or to avoid potential side effects such as thrombocytopenia and neutropenia. CLINICAL TRIALS REGISTRATION NUMBERS: NV15942 and NV15801.

Predictors of Severe Thrombocytopenia Secondary to Peginterferon Alfa-2a Treatment in Subjects With Hepatitis C Virus Infection.

In this study, we aim to identify patient characteristics that predict severe thrombocytopenia induced by peginterferon alfa-2a in hepatitis C virus-infected patients. Demographic, clinical, and genetic data collected from patients with chronic hepatitis C virus infection (n = 232; age ≥18 years) who received peginterferon alfa-2a following eltrombopag treatment. Predictors of severe thrombocytopenia (platelet count below 50 GI/L) were identified using a 2-step approach: First, univariate analysis, using χ test for categorical variables and t test for continuous variables, was performed to identify possible predictors of severe thrombocytopenia (P < 0.05). Second, a logistic regression with backward stepwise selection was then performed using predictors identified in univariate analysis step to produce final model containing independent predictors at P < 0.05. Logistic model identified several predictors of severe thrombocytopenia. Increased spleen length and increased alkaline phosphatase levels increases the likelihood of severe thrombocytopenia. However, being Central/South Asian, increased neutrophils count and increased platelet baseline count decreases the probability of developing severe thrombocytopenia. In summary, we identified several patient characteristics that predict severe thrombocytopenia induced by peginterferon alfa-2a. Early selection of individuals with high risk of developing interferon-associated severe thrombocytopenia allows early intervention (such as eltrombopag treatment). Early intervention in turn minimizes the odds of developing severe thrombocytopenia and allows the continual of antiviral therapy before patient progress into liver decompensation.

Clinical Predictors Associated With Warfarin Sensitivity.

Our study aimed to identify predictors of warfarin sensitivity like demographic, clinical, and genetic data from a previously collected cohort of patients (n = 4272) with a stable warfarin dose who were able to achieve an observed international normalized ratio of 2-3. Predictors of warfarin sensitivity (dose ≤21 mg/wk) were identified using a 2-stage approach. First, bivariate analysis, using analysis of variance for continuous variables and χ test for categorical variables, was performed to identify possible predictors of warfarin sensitivity (P < 0.05). Second, logistic regression with backward stepwise selection was then performed using predictors identified in bivariate analysis step to produce final model containing independent predictors at P < 0.05. Increased warfarin sensitivity was associated with increased age; CYP2C9 genotypes 2/3, 1/3, and 3/3; VKORC1 genotypes AA and AG; and amiodarone use. Decreased warfarin sensitivity (ie, weekly warfarin dose of >21 mg) was associated with increased height, increased weight, having diabetes mellitus, VKORC1 genotype GG, and CYP2C9 genotype 1/1. In conclusion, we identified patients' characteristics associated with warfarin sensitivity. This project is expected to improve patient care by identifying patients who need a low warfarin dose before warfarin administration. Early identification of this subset of patients helps minimize the incidence of bleeding.

Pharmacogenetic-guided Warfarin Dosing Algorithm in African-Americans.

We aim to develop warfarin dosing algorithm for African-Americans. We explored demographic, clinical, and genetic data from a previously collected cohort of 163 African-American patients with a stable warfarin dose. We explored 2 approaches to develop the algorithm: multiple linear regression and artificial neural network (ANN). The clinical significance of the 2 dosing algorithms was evaluated by calculating the percentage of patients whose predicted dose of warfarin was within 20% of the actual dose. Linear regression model and ANN model predicted the ideal dose in 52% and 48% of the patients, respectively. The mean absolute error using linear regression model was estimated to be 10.8 mg compared with 10.9 mg using ANN. Linear regression and ANN models identified several predictors of warfarin dose including age, weight, CYP2C9 genotype *1/*1, VKORC1 genotype, rs12777823 genotype, rs2108622 genotype, congestive heart failure, and amiodarone use. In conclusion, we developed a warfarin dosing algorithm for African-Americans. The proposed dosing algorithm has the potential to recommend warfarin doses that are close to the appropriate doses. The use of more sophisticated ANN approach did not result in improved predictive performance of the dosing algorithm except for patients of a dose of ≥49 mg/wk.

Eltrombopag dose predictors in thrombocytopenic subjects with hepatitis C virus infection.

This study aims to identify patient characteristics that predict effective eltrombopag dosage for the treatment of Hepatitis C virus (HCV)-related thrombocytopenia. Demographic, clinical and genetic data collected from thrombocytopenic patients (n = 1463, age ≥ 18 years) with chronic HCV infection who were able to achieve a target platelet count of > 90 × 10(9) /L following eltrombopag treatment. Patients were categorized into four groups (25, 50, 75, and 100 mg) based on the eltrombopag dose needed to achieve the target platelet count. Eltrombopag dose predictors were identified using a two stage approach. First, bivariate analysis, using anova for continuous variables and Chi-square test for categorical variables, was performed to identify possible predictors of eltrombopag dose (P < 0.05). Second, ordinal logistic regression with stepwise addition followed by backward deletion was then performed using predictors identified in bivariate analysis step to produce final model containing independent predictors at P < 0.05. Ordinal logistic model identified several predictors of eltrombopag dose. Predictors of higher eltrombopag dose include: having a HCV genotype 2 or 3, being Central/South Asian, being White (Caucasian or European heritage), increased weight, and increased spleen length. Predictors of lower eltrombopag dose include: female gender, increased age, having a higher ALP plasma concentration, increased creatinine clearance, increased baseline lymphocytes count, and increased baseline platelet count. In conclusion, this study identified patient characteristics that predict effective eltrombopag dose for the treatment of HCV-related thrombocytopenia. Early selection of the optimal eltrombopag dose expedites the initiation of antiviral therapy. This is expected to improve the antiviral therapy outcome before the patient progress into liver decompensation.

Dosage individualization of warfarin using artificial neural networks.

BACKGROUND AND OBJECTIVES: Our objective was to explore artificial neural networks (ANNs) as a possible tool for dosage individualization of warfarin. METHODS: Demographic, clinical, and genetic data were gathered from a previously collected cohort of patients with a stable warfarin dosage who were able to achieve an observed international normalized ratio of 2-3. Data from a cohort of 3,415 patients were used to develop an ANN dosing algorithm. Data from another cohort of 856 were used to validate the algorithm. The clinical significance of the ANN dosing algorithm was evaluated by calculating the percentage of patients whose predicted dosage of warfarin was within 20 % of the actual stable therapeutic dose. The clinical significance was also compared with a previously published dosing algorithm. RESULTS: A feed-forward neural network with three layers was able to successfully predict the ideal warfarin dosage in 48 % of the patients. The neural network model explained 48 % and 43 % of the dosage variability observed among patients in the derivation and validation cohorts, respectively. ANN analysis identified several predictors of warfarin dosage including race; age; height; weight; cytochrome P450 (CYP)2C9 genotype; VKORC1 genotype; sulfonamide, azole antifungals, or macrolide administration; carbamazepine, phenytoin, or rifampicin administration; and amiodarone administration. CONCLUSION: An ANN was applied to develop a warfarin dosing algorithm. The proposed dosing algorithm has the potential to recommend warfarin dosages that are close to the appropriate dosages.

Population pharmacodynamic analysis of erythropoiesis in preterm infants for determining the anemia treatment potential of erythropoietin.

A population pharmacokinetics/pharmacodynamic (PK/PD) model was developed to describe changes in erythropoiesis as a function of plasma erythropoietin (EPO) concentration over the first 30 days of life in preterm infants who developed severe anemia requiring red blood cell (RBC) transfusion. Several covariates were tested as possible factors influencing the responsiveness to EPO. Discarded blood samples in 27 ventilated preterm infants born at 24-29 wk of gestation were used to construct plasma EPO, hemoglobin (Hb), and RBC concentration-time profiles. The amount of Hb removed for laboratory testing and that transfused throughout the study period were recorded. A population PK/PD model accounting for the dynamic Hb changes experienced by these infants was simultaneously fitted to plasma EPO, Hb, and RBC concentrations. A covariate analysis suggested that the erythropoietic efficacy of EPO is increased for preterm infants at later gestational ages. The PD analysis showed a sevenfold difference in maximum Hb production rate dependent on gestational age and indicated that preterm infants, when stimulated by EPO, have the capacity to produce additional Hb that may result in a decrease in RBC transfusions. The present model has utility in clinical trial simulations investigating the treatment potential of erythropoietic stimulating agents in the treatment of anemia of prematurity.

Pharmacodynamic analysis of stress erythropoiesis: change in erythropoietin receptor pool size following double phlebotomies in sheep.

A feedback receptor regulation model was incorporated into a pharmacodynamic model to describe the stimulation of hemoglobin (Hb) production by endogenous erythropoietin (EPO). The model considers the dynamic changes that take place in the EPO receptor (EPOR) pool under phlebotomy-induced anemia. Using a (125)I-rhEPO tracer the EPO clearance changes are evaluated longitudinally prior to and following phlebotomy-induced anemia indirectly to evaluate changes in the EPOR pool size, which has been shown to be linearly related to the clearance. The proposed model simultaneously captures the general behavior of temporal changes in Hb relative to EPO plasma clearance in five lambs (r = 0.95), while accounting for the confounding variables of phlebotomy and changes in the blood volume in the growing animals. The results indicate that under anemia the EPOR pool size is up-regulated by a factor of nearly two over baseline and that the lowest and highest EPOR pool sizes differ by a factor of approximately four. The kinetic model developed and the data-driven mechanism proposed serves as a starting point for developing an optimal EPO dosing algorithm for the treatment of neonatal anemia.