Pharmacometrics

Aims: Pharmacological treatment of chronic (neuropathic) pain is often disappointing. In order to enhance our insight in the complex interaction between analgesic drug and chronic pain relief, we performed a pharmacokinetic-pharmacodynamic (PK-PD) modeling study on the effect of S(+)-ketamine on pain scores in Complex Regional Pain Syndrome type 1 (CRPS-1) patients. Methods: Sixty CRPS-1 patients were randomly allocated to received a 100-h infusion of S(+)-ketamine or placebo. The drug infusion rate was slowly increased from 5 mg/h (per 70 kg) to 20 mg/h based upon the effect/side effect profile. Pain scores and drug blood samples were obtained during the treatment phase and pain scores were further obtained weekly for another 11 weeks. A population PK-PD model was developed to analyze the S(+)-ketamine-pain data. Results: Plasma concentrations of S(+)-ketamine and its metabolite decreased rapidly upon the termination of S(+)-ketamine infusion. The chance for an analgesic effect from ketamine and placebo treatment was 67 ± 10% and 23 ± 9% (population value ± SE), respectively. The pain data were well described by the PK-PD model with parameters C 50 = 10.5 ± 4.8 ng/ml (95% ci 4.37-21.2 ng/ml) and t½ for onset/offset = 10.9 ± 4.0 days (5.3-20.5 days). Discussion: Long-term S(+)-ketamine treatment is effective in causing pain relief in CRPS-1 patients with analgesia outlasting the treatment period by 50 days. These data suggest that ketamine initiated a cascade of events, including desensitization of excitatory receptor systems in the central nervous system, which persisted but slowly abated when ketamine molecules were no longer present.

NONMEM® is the most-widely used nonlinear mixed effects modelling tool introduced into population PK/PD analysis. Even though thousands of pharmaceutical scientists utilize NONMEM® routinely for their data analysis, the various estimation methods implemented in NONMEM® remain a mystery for most users due to the complex statistical and mathematical derivations underlying the algorithm used in NONMEM®. In this tutorial, we demonstrated how to directly obtain the objective function value and post hoc η for the first order approximation method by the use of R. We hope that this tutorial helps pharmacometricians understand the underlying estimation process of nonlinear mixed effects modelling.

Context: Ivermectin (IVM) and fluazuron are present as novel combinations to control ticks. Differences in formulations account for changes to the plasma kinetics and could change exposure of target parasites to active drugs. Aims: To evaluate bioequivalence for ivermectin administered by two novel formulations combined with fluazuron. Methods: Twelve male Holstein calves were randomized into two groups (n=6), receiving a single subcutaneous dose of a novel formulation (A or B) of ivermectin combined with fluazuron (ivermectin 1% 0.2 mg/kg + fluazuron 12.5% 2.5 mg/kg). Blood samples were taken until 34 days after dosing. Non-compartmental analysis was applied for bioequivalence assessment. Compartmental analysis was carried out and model acceptance was validated using the visual predictive check graphics. Results: Noncompartmental analysis shows that both formulations behaved similarly with Cmax ratio of 0.982 (CI90: 0.861-1.12, ANOVA test, p=0.814) and AUC0-t ratio of 1.01 (CI90: 0.97-1.05, ANOVA, p=0.586) but at different Tmax (1.4 ± 0.5 and 2.9 ± 1.2 days (Mann-Whitney U test, p=0.027) for A and B, respectively). The pharmacokinetic model has two compartments, linear elimination with first order absorption and formulation as covariable for the absorption rate. Conclusions: We conclude that the two novel formulations combined of IVM 1% with fluazuron 12.5%, are bioequivalent for administration of IVM in cattle. This shows that carrying out bioequivalence studies are of great importance for understanding the potential interchangeability between formulations available in the pharmaceutical market.

Drug development continues to be expensive, time consuming, and ineffi cient, while pharmacotherapy is often practiced at suboptimal levels of performance (1-3). This trend has not waned despite the fact that massive amounts of drug data are obtained each year. Within these massive amounts of data, knowledge that would improve drug development and pharmacotherapy lays hidden and undiscovered. The application of pharmacometric (PM) principles and models to drug development and pharmacotherapy will signifi cantly improve both (4, 5). Furthermore, with drug utilization review, generic competition, managed care organization bidding, and therapeutic substitution, there is increasing pressure for the drug development industry to deliver high-value therapeutic agents. The Food and Drug Administration (FDA) has expressed its concern about the rising cost and stagnation of drug development in the white paper Challenge and Opportunity on the Critical Path to New Products published in March of 2004 (3). In this document the FDA states: "Not enough applied scientifi c work has been done to create new tools to get fundamentally better answers about how the safety and effectiveness of new products can be demonstrated in faster time frames, with more certainty, and at lower costs.. .. A new product development toolkit-containing powerful new scientifi c and technical methods such as animal or computer-based predictive models, biomarkers for safety and effectiveness, and new clinical evaluation techniques-is urgently needed to improve predictability and effi ciency along the critical path from laboratory concept to commercial product. We need superior product development science to address these challenges." In the critical path document, the FDA states that the three main areas of the path that need to be addressed are tools for assessing safety, tools for demonstrating medical utility, and lastly tools for characterization and manufacturing. Pharmacometrics can be applied to and can impact the fi rst two areas, thus positively impacting the critical path.

Identifying potential adverse drug reactions (ADRs) is critically important for drug discovery and public health. Here we developed a multiple evidence fusion (MEF) method for the large-scale prediction of drug ADRs that can handle both approved drugs and novel molecules. MEF is based on the similarity reference by collaborative filtering, and integrates multiple similarity measures from various data types, taking advantage of the complementarity in the data. We used MEF to integrate drug-related and ADR-related data from multiple levels, including the network structural data formed by known drug–ADR relationships for predicting likely unknown ADRs. On cross-validation, it obtains high sensitivity and specificity, substantially outperforming existing methods that utilize single or a few data types. We validated our prediction by their overlap with drug–ADR associations that are known in databases. The proposed computational method could be used for complementary hypothesis generation and rapid analysis of potential drug–ADR interactions.

Drug development continues to be expensive, time consuming, and ineffi cient, while pharmacotherapy is often practiced at suboptimal levels of performance (1-3). This trend has not waned despite the fact that massive amounts of drug data are obtained each year. Within these massive amounts of data, knowledge that would improve drug development and pharmacotherapy lays hidden and undiscovered. The application of pharmacometric (PM) principles and models to drug development and pharmacotherapy will signifi cantly improve both (4, 5). Furthermore, with drug utilization review, generic competition, managed care organization bidding, and therapeutic substitution, there is increasing pressure for the drug development industry to deliver high-value therapeutic agents. The Food and Drug Administration (FDA) has expressed its concern about the rising cost and stagnation of drug development in the white paper Challenge and Opportunity on the Critical Path to New Products published in March of 2004 (3). In this document the FDA states: "Not enough applied scientifi c work has been done to create new tools to get fundamentally better answers about how the safety and effectiveness of new products can be demonstrated in faster time frames, with more certainty, and at lower costs.. .. A new product development toolkit-containing powerful new scientifi c and technical methods such as animal or computer-based predictive models, biomarkers for safety and effectiveness, and new clinical evaluation techniques-is urgently needed to improve predictability and effi ciency along the critical path from laboratory concept to commercial product. We need superior product development science to address these challenges." In the critical path document, the FDA states that the three main areas of the path that need to be addressed are tools for assessing safety, tools for demonstrating medical utility, and lastly tools for characterization and manufacturing. Pharmacometrics can be applied to and can impact the fi rst two areas, thus positively impacting the critical path.

Aim: This study aims to evaluate the antiarthritic activity of chloroform and ethanolic extracts of Synedrella nodiflora. Materials and Methods: Plant was collected and then powdered; the powdered plant was then cold macerated with 70% v/v of ethanol and chloroform for 48 h and 72 h, respectively. Both extracts, after the subsequent time, were concentrated to a syrupy mass in Soxhlet apparatus under reduced pressure and desiccated. Protein denaturation assay is based on the effect of plant extract to inhibit the happening of denaturation of protein from heat. We evaluated the potency of the plant extract to encumber the denaturation of protein. It was calculated in percentage and absorbance was studied at 660 nm. Results and Discussion: On increasing concentrations, it exhibited different percentage inhibition. Among chloroform and ethanol extract, ethanolic extract was found to be equally effective when compared to standard drug diclofenac sodium. In antidenaturation assay, the effect of plant extract was determined on the basis of its potential to inhibit the denaturation of the protein. Conclusion: Study indicates that the plant extract has the potency to exhibit antiarthritic activity and can be used to proceed with further in vivo studies and also the isolation of principle constituent.

OATP1B3 is a member of the OATP (organic anion transporting polypeptides) superfamily, responsible for mediating the transport of numerous endogenous and xenobiotic substances. Although initially reported to be exclusively expressed in the liver, several studies reported that OATP1B3 is frequently expressed in multiple types of cancers and may be associated with differing clinical outcomes. However, a detailed investigation on the expression and function of OATP1B3 protein in cancer has been lacking. In this study, we confirmed that colon and pancreatic cancer cells express variant forms of OATP1B3, different from OATP1B3 wild-type (WT) expressed in the normal liver. OATP1B3 variant 1 (V1), the most prevalent form among the variants, contains alternative exonic sequences (exon 2a) instead of exons 1 and 2 present in OATP1B3 WT. The translated product of OATP1B3 V1 is almost identical to OATP1B3 WT, with exception to the first 28 amino acids at the N-terminus. Exogenous expression of OATP1B3 V1 revealed that OATP1B3 V1 undergoes post-translational modifications and proteasomal degradation to a differing extent compared to OATP1B3 WT. OATP1B3 V1 showed only modest transport activity toward cholecystokin-8 (CCK-8, a prototype OATP1B3 substrate) in contrast to OATP1B3 WT showing a markedly efficient uptake of CCK-8. Consistent with these results, OATP1B3 V1 was localized mainly in the cytoplasm with a much lower extent of trafficking to the surface membrane compared to OATP1B3 WT. In summary, our results demonstrate that colon and pancreatic cancer cells express variant forms of OATP1B3 with only limited transport activity and different subcellular localization compared to OATP1B3 WT. These observed differences at the molecular and functional levels will be important considerations for further investigations of the biological and clinical significance of OATP1B3 expression in cancer.