Pharmacokinetic and pharmacokinetic/pharmacodynamic characterization of the dolutegravir/rilpivirine two-drug regimen in SWORD-1/-2 phase 3 studies.

AIM: SWORD-1 and SWORD-2 phase 3 studies concluded that switching virologically suppressed participants with HIV-1 from their current three- or four-drug antiretroviral regimen (CAR) to the two-drug regimen of once-daily dolutegravir (DTG, 50 mg) and rilpivirine (RPV, 25 mg) was safe, well tolerated and noninferior for maintaining HIV-1 suppression at week 48 and highly efficacious to week 148. A secondary objective was to characterize drug exposure and exposure-efficacy/safety relationships. METHODS: Adults with plasma HIV-1 RNA <50 copies/mL were randomized to switch to once-daily DTG + RPV on day 1 or to continue CAR for 52 weeks before switching. Trough plasma concentrations (C0) of DTG and RPV, the proportion of participants with HIV-1 RNA <50 copies/mL and adverse events to week 100 were summarized and subjected to exposure-response analyses in the overall population, in the subset of participants who switched from CAR containing enzyme-inducing drugs and by age category (≥50 and <50 years). The relationship between C0avg (individual average C0 across visits) and efficacy/safety was investigated. RESULTS: Although week 2 DTG and RPV C0 were lower in participants switching from enzyme-inducing antiretroviral drugs, C0 and C0avg stayed above in vitro antiviral protein binding-adjusted IC90 and to week 100 with viral suppression >89%. DTG or RPV C0avg showed no relationship with virologic failures or safety. Participants ≥50 years had similar C0avg and safety response to younger participants. CONCLUSION: No clinically relevant relationship between DTG or RPV exposures and virologic or safety response was observed, confirming the DTG + RPV switch for participants as a safe and effective treatment.

Current strategies to streamline pharmacotherapy for older adults.

Although the term "personalized medicine" has been associated in many cases with pharmacogenomics, its definition embraces the use of specific biomarkers and covariates to help in the selection of medical treatments and procedures which are best for each patient. While several efforts have been performed for the tailored selection of therapies and dosing regimens in the general population, developing personalized medicine initiatives for elderly patients remains understudied. The personalized drug therapy for older patients requires the consideration of anatomical, physiological and functional alterations in a multimorbid setting requiring multiple medications. The present review focuses on currently employed qualitative and quantitative precision medicine approaches for elderly patients and discusses some of the associated challenges and limitations. Furthermore, the use of and confidence in physiologically-based approaches for optimal dose selection in this understudied yet clinically important patient population will be highlighted and discussed.

How Informative Are Drug-Drug Interactions of Gene-Drug Interactions?

FDA recommendations to manage polymorphic CYP-mediated drug-drug interactions (DDIs) and gene-drug interactions (GDIs) are typically similar. However, DDIs may not always reliably predict GDIs because the victim drug may have multiple metabolic pathways and the perpetrator drug may affect multiple enzymes or transporters. Consequently, it is of great interest to both the pharmaceutical industry and regulatory agencies to determine if DDI studies can be leveraged to inform GDIs or vice versa for dose adjustment and labeling. The objective of this study was to investigate under what circumstances DDIs can be used to predict GDIs for prototypical CYP2C9, CYP2C19, and CYP2D6 substrates. We investigated model substrates for CYP2D6 (metoprolol, dextromethorphan, atomoxetine, and vortioxetine), CYP2C9 (warfarin, flurbiprofen, and celecoxib), and CYP2C19 (omeprazole and clopidogrel). Data on drug exposure for poor metabolizers (GDI) and for DDIs mediated by strong/moderate inhibitors in extensive metabolizers were collected. The impact of DDIs and GDIs on drug exposure was compared using: (1) a descriptive and (2) a physiologically based pharmacokinetic convergence analysis. Results from both approaches indicate that information on DDIs can be used to reliably predict GDIs for CYP2D6 substrates. The situation is more complex for CYP2C9 and CYP2C19 substrates because dose of the inhibitor (CYP2C9) and potency of the inhibitor (CYP2C19) impact the extent to which perpetrator drugs phenotypically convert extensive metabolizers to poor(er) metabolizers.

Simultaneous Semimechanistic Population Analyses of Levofloxacin in Plasma, Lung, and Prostate To Describe the Influence of Efflux Transporters on Drug Distribution following Intravenous and Intratracheal Administration.

Levofloxacin (LEV) is a broad-spectrum fluoroquinolone used to treat pneumonia, urinary tract infections, chronic bacterial bronchitis, and prostatitis. Efflux transporters, primarily P-glycoprotein (P-gp), are involved in LEV's tissue penetration. In the present work, LEV free lung and prostate interstitial space fluid (ISF) concentrations were evaluated by microdialysis in Wistar rats after intravenous (i.v.) and intratracheal (i.t.) administration (7 mg/kg of body weight) with and without coadministration of the P-gp inhibitor tariquidar (TAR; 15 mg/kg administered i.v.). Plasma and tissue concentration/time profiles were evaluated by noncompartmental analysis (NCA) and population pharmacokinetics (popPK) analysis. The NCA showed significant differences in bioavailability (F) for the control group (0.4) and the TAR group (0.86) after i.t. administration. A four-compartment model simultaneously characterized total plasma and free lung (compartment 2) and prostate (compartment 3) ISF concentrations. Statistically significant differences in lung and prostate average ISF concentrations and levels of kidney active secretion in the TAR group from those measured for the control group (LEV alone) were observed. The estimated population means were as follows: volume of the central compartment (V1), 0.321 liters; total plasma clearance (CL), 0.220 liters/h; TAR plasma clearance (CLTAR), 0.180 liters/h. The intercompartmental distribution rate constants (K values) were as follows: K12, 8.826 h(-1); K21, 7.271 h(-1); K13, 0.047 h(-1); K31, 7.738 h(-1); K14, 0.908 h(-1); K41, 0.409 h(-1); K21 lung TAR (K21LTAR), 8.883 h(-1); K31 prostate TAR (K31PTAR), 4.377 h(-1). The presence of P-gp considerably impacted the active renal secretion of LEV but had only a minor impact on the efflux from the lung following intratracheal dosing. Our results strongly support the idea of a role of efflux transporters other than P-gp contributing to LEV's tissue penetration into the prostrate.

Evaluation of Approaches to Deal with Low-Frequency Nuisance Covariates in Population Pharmacokinetic Analyses.

Clinical studies include occurrences of rare variables, like genotypes, which due to their frequency and strength render their effects difficult to estimate from a dataset. Variables that influence the estimated value of a model-based parameter are termed covariates. It is often difficult to determine if such an effect is significant, since type I error can be inflated when the covariate is rare. Their presence may have either an insubstantial effect on the parameters of interest, hence are ignorable, or conversely they may be influential and therefore non-ignorable. In the case that these covariate effects cannot be estimated due to power and are non-ignorable, then these are considered nuisance, in that they have to be considered but due to type 1 error are of limited interest. This study assesses methods of handling nuisance covariate effects. The specific objectives include (1) calibrating the frequency of a covariate that is associated with type 1 error inflation, (2) calibrating its strength that renders it non-ignorable and (3) evaluating methods for handling these non-ignorable covariates in a nonlinear mixed effects model setting. Type 1 error was determined for the Wald test. Methods considered for handling the nuisance covariate effects were case deletion, Box-Cox transformation and inclusion of a specific fixed effects parameter. Non-ignorable nuisance covariates were found to be effectively handled through addition of a fixed effect parameter.

A reduction in between subject variability is not mandatory for selecting a new covariate.

Population pharmacokinetic-pharmacodynamic analysis involves nonlinear hierarchical modelling where the mean response in a population and the variability in response from different sources are studied. It generally consists of two model hierarchies: a model for residual error and a model for heterogeneity termed between subject variance (BSV). The overall variability in a parameter within a population termed population parameter variance (PPV) consists of within subject variance (WSV) and BSV. Both these variances can further be split into random and predictable components. The predictable component of BSV (termed BSVP) is explained by covariates, individual characteristics e.g. weight. As BSVP increases, the remaining unpredictable (or random) between subject variability (BSVR) decreases since BSV = BSVP + BSVR, and BSV is a constant in any given data set. Since BSV and BSVR are estimated from the base and full covariate models, respectively, then BSVP = BSV-BSVR. The aim of this study was to explore the hypothesis, that a significant covariate may not always decrease BSVR. The specific aims were: (1) to explore circumstances where BSVR may not be reduced when adding a significantly correlated covariate and (2) to explore whether specific models for covariates may eliminate this anomaly when assessing BSVR. Simulations were performed using MATLAB (2011a) and estimation using NONMEM (ver 7.2) with FOCE and INTERACTION. A 1-compartment intravenous bolus PK model was used for simulation following a single unit dose (d = 1). The BSV of clearance [BSV(CL)] was described according to a log-normal distribution model with mean zero and variance ω². An additive random unexplained variability was assumed. Initially, we show through a simple simulation that BSVR can increase when a significantly correlated covariate is added to the model. We follow this with five simulation scenarios, A to E, that have various levels of correlation between the continuous covariate (Z) and CL ranging from 0 to 100 %. Each simulated scenario was replicated 100 times and estimated by a base model (i.e. without covariate addition) and six covariate models (M1-M6) which included non-nested (M1), nested (M2), and two types of interaction models for each of M1 and M2; non-nested interaction (M3, M5), nested interaction (M4, M6). Initially, through a motivating example we show that BSVR may not reduce even when there is 50 % correlation between the covariate Z and CL. It was found that with 0 % correlation M1, the non-nested covariate model (NNCM) resulted in negative BSVP (inflated BSVR) whereas M2, the nested covariate model (NCM), resulted in a calculated BSVP of zero. NNCM (M1) shows negative BSVP (BSVR > BSV) with correlation as high as 50 % and this model needs a minimum of 75 % correlation to show a positive BSVP. NCM (M2) shows positive but downwardly biased BSVP with 25, 50 and 75 % correlations. However, inclusion of a covariate-eta interaction term for both types of covariate models resulted in greater BSVP for 25, 50 and 75 % correlation scenarios compared to NNCM and NCM respectively. For 100 % correlation, it was found that covariate-eta interaction models show the same BSVP as the models without the interaction term, i.e. under perfect positive correlation all models perform similarly and correctly. It was found that a significantly correlated covariate may not reduce BSVR and in fact it may inflate the BSVR due to statistical misspecification of the covariate model. Incorporating statistical models that account for the covariate-eta interaction may be useful diagnostically in identifying the variability explained by covariates.

Design of pharmacokinetic studies for latent covariates.

Latent covariates are covariates that are known to exist but are either observable but unavailable or unobservable at the time of the clinical study. Designs to account for latent covariates must incorporate both uncertainty in the prevalence of the covariate and the data-type of the covariate. The informativeness of the covariate will then depend on whether the covariate data is continuous, ordinal or nominal. In this work we consider designs for latent covariates that may either directly influence the parameter of interest, or indirectly via actions on an observable covariate which then influences the parameter of interest. We consider a motivating example based on the effect of a genetic polymorphism on the influence of a continuous covariate (age) on drug clearance (CL). The polymorphism could take the case of a haplotype with many variant alleles, or a copy number variation in genes with different phenotypic expressions which could be treated as continuous data, or as a bi- or tri-allelic single nucleotide polymorphism that could form either an ordinal or nominal covariate on drug CL. The aim of this study was to investigate designs for clinical studies for latent covariates that accommodate both unknown prevalence and unknown data-type. Initially, the informativeness of a covariate was explored using linear regression assuming the three data-types continuous, ordinal and nominal. The linear covariate model was then considered within a nonlinear mixed effects modelling framework. Two simulation scenarios were considered: (1) the influence of the latent covariate directly on the parameter of interest and (2) the influence of the latent covariate on an observable non-latent continuous covariate, which was assumed to follow a normal or stratified distribution, and the effect of this covariate on the parameter of interest. A power analysis for population PK modelling (1) where the latent covariate had direct influence on the parameter also showed similar behaviour to the linear regression solution. When the influence of the latent covariate was mediated via another observable non-latent continuous covariate, the power for the continuous model was highest but the power of the ordinal model was indistinguishable from that of the nominal model. Stratification of the observable non-latent continuous covariate did not appreciably change the power to identify the latent covariate from that when we assumed the observable covariate conformed to a normal distribution. It was found that parameter estimation is generally at least 1.5 to 7 fold more precise for continuous models than for categorical models.

Polyamines: Potential anti-inflammatory agents and their possible mechanism of action.

OBJECTIVE: To evaluate the anti-inflammatory activity of exogenously administered polyamines on experimentally induced acute and chronic inflammation in wistar rats and to elucidate their possible mechanism of action. MATERIALS AND METHODS: The in vivo anti-inflammatory activity of polyamines was studied using acute (carrageenin paw edema), sub-acute (cotton pellet granuloma) and chronic (Freund's adjuvant induced arthritis) models of inflammation. The biochemical parameters like liver lipid peroxides, SGOT and SGPT were also measured. RESULTS: Polyamines exhibited significant anti-inflammatory activity in acute, sub-acute and chronic models of inflammation. Polyamines treatment inhibited the increase in lipid peroxides in liver and the serum concentration of marker enzymes (glutamate oxaloacetate transferase and glutamate pyruvate transferase) during inflammation. CONCLUSION: Polyamines possess anti-inflammatory activity in acute and chronic inflammation which can be attributed to their anti-oxidant and /or lysosomal stabilization properties.

An experimental evaluation of the antidiabetic and antilipidemic properties of a standardized Momordica charantia fruit extract.

BACKGROUND: The MCE, Momordica charantia fruit extract Linn. (Cucurbitaceae) have been documented to elicit hypoglycemic activity on various occasions. However, due to lack of standardization of these extracts, their efficacy remains questionable. The present study was undertaken by selecting a well standardised MCE. This study reports hypoglycemic and antilipidemic activities of MCE employing relevant animal models and in vitro methods. METHODS: Diabetes was induced in Wistar rats by a s.c., subcutaneous injection of alloxan monohydrate (100 mg/kg) in acetate buffer (pH 4.5). MCE and glibenclamide were administered orally to alloxan diabetic rats at doses of 150 mg/kg, 300 mg/kg & 600 mg/kg, and 4 mg/kg respectively for 30 days, blood was withdrawn for glucose determination on 0, 7, 14, 21 and 30th days. On the 31st day, overnight fasted rats were sacrificed and blood was collected for various biochemical estimations including glycosylated haemoglobin, mean blood glucose, serum insulin, cholesterol, triglcerides, protein and glycogen content of liver. The hemidiaphragms and livers were also isolated, carefully excised and placed immediately in ice cooled perfusion solution and processed to study the glucose uptake/transfer processes. Hypolipidemic activity in old obese rats was evaluated by treating two groups with MCE (150 mg/kg & 300 mg/kg) orally for 30 days and determining total cholesterol, triglyceride and HDL-CH, LDL-CH and VLDL-CH levels from serum samples. RESULTS: Subchronic study of MCE in alloxan induced diabetic rats showed significant antihyperglycemic activity by lowering blood glucose and GHb%, percent glycosylated haemoglobin. Pattern of glucose tolerance curve was also altered significantly. MCE treatment enhanced uptake of glucose by hemidiaphragm and inhibited glycogenolysis in liver slices in vitro. A significant reduction in the serum cholesterol and glyceride levels of obese rats following MCE treatment was also observed. CONCLUSION: Our experimental findings with respect to the mechanism of action of MCE in alloxan diabetic rats suggest that it enhances insulin secretion by the islets of Langerhans, reduces glycogenesis in liver tissue, enhances peripheral glucose utilisation and increases serum protein levels. Furthermore, MCE treatment restores the altered histological architecture of the islets of Langerhans. Hence, the biochemical, pharmacological and histopathological profiles of MCE clearly indicate its potential antidiabetic activity and other beneficial effects in amelioration of diabetes associated complications. Further, an evaluation of its antilipidemic activity in old obese rats demonstrated significant lowering of cholesterol and triglyceride levels while elevating HDL-cholesterol levels. Also, the extract lowered serum lipids in alloxan diabetic rats, suggesting its usefulness in controlling metabolic alterations associated with diabetes.