Drug repurposing: Iron in the fire for older drugs.

Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and limited to a fairly small number of targets. The incorporation of separate and complementary data should be used, as each type of data set exposes a specific feature of organism knowledge Drug repurposing opportunities are often focused on sporadic findings or on time-consuming pre-clinical drug tests which are often not guided by hypothesis. In comparison, repurposing in-silico drugs is a new, hypothesis-driven method that takes advantage of big-data use. Nonetheless, the widespread use of omics technology, enhanced data storage, data sense, machine learning algorithms, and computational modeling all give unparalleled knowledge of the methods of action of biological processes and drugs, providing wide availability, for both disease-related data and drug-related data. This review has taken an in-depth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning.

Evaluation and calibration of high-throughput predictions of chemical distribution to tissues.

Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (Kp) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat Kp measured by in vivo experiments for 143 compounds. Initially, predicted Kp were significantly larger than measured Kp for many lipophilic compounds (log10 octanol:water partition coefficient > 3). Hence the approach for predicting Kp was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 Kp across 23 compounds with only 3 Kp worsening by more than a factor of 10. The vast majority (92%) of Kp were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log10-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (Vss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R2 of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.

In silico pharmacodynamics, toxicity profile and biological activities of the Saharan medicinal plant Limoniastrum feei

ABSTRACT In-silico study was performed to find the pharmacodynamics, toxicity profiles and biological activities of three phytochemicals isolated from Limoniastrum feei (Plumbagenaceae). Online pharmacokinetic tools were used to estimate the potential of Quercetin, kaempferol-3-O-ß-D-glucopyranoside (astragalin) and quercitin-7-O-ß-D-glucopyranoside as specific drugs. Then the prediction of potential targets of these compounds were investigated using PharmMapper. Auto-Dock 4.0 software was used to investigate the different interactions of these compounds with the targets predicted earlier. The permeability of quercetin was found within the range stated by Lipinski ׳s rule of five. Hematopoietic prostaglandin (PG) D synthase (HPGDS), farnesyl diphosphate synthetase (FPPS) and the deoxycytidine kinase (DCK) were potential targets for quercetin, astragalin and quercetin 7, respectively. Quercetin showed antiallergic and anti-inflammatory activity, while astragalin and quercetin 7 were predicted to have anticancer activities. The activity of Astragalin appeared to be mediated by FPPS inhibition. The inhibition of DCK was predicted as the anticancer mechanisms of quercetin 7. The compounds showed interesting interactions and satisfactory binding energies when docked into their targets. These compounds are proposed to have activities against a variety of human aliments such as allergy, tumors, muscular dystrophy, and diabetic cataracts.

Clinical Trial Simulations From a Model-Based Meta-Analysis of Studies in Patients With Advanced Hepatocellular Carcinoma Receiving Antiangiogenic Therapy.

A mixed effect model describing median overall survival (mOS) in patients with advanced hepatocellular carcinoma (aHCC) treated with antiangiogenic therapy (AAT) was developed from literature data. Data were extracted from 59 studies, representing 4,813 patients. The final model included estimates of mOS after AAT (8.5 months) or placebo (7.1 months) administration. The mOS increased 21% when the AAT was sorafenib (SOR) or 42% when locoregional therapy was coadministered. The mOS decreased when patients received prior systemic therapy (↓7%) or concomitant chemotherapy (↓4%) or the percentage of patients with hepatitis B increased (↓∼0.4%/%). Clinical trial simulations of a phase II comparative trial predicted an mOS ratio (placebo:AAT) of 0.687 or 0.831, with a 65% or 22% probability of demonstrating superiority, for SOR or other AATs, respectively. Additionally, the 95% confidence interval (CI) of the simulated median mOS ratio for non-SOR AATs was similar to the 95% CI of the hazard ratio (HR) observed in the trial.

Coding accuracy is not fully determined by the neuronal model.

It is automatically assumed that the accuracy with which a stimulus can be decoded is entirely determined by the properties of the neuronal system. We challenge this perspective by showing that the identification of pure tone intensities in an auditory nerve fiber depends on both the stochastic response model and the arbitrarily chosen stimulus units. We expose an apparently paradoxical situation in which it is impossible to decide whether loud or quiet tones are encoded more precisely. Our conclusion reaches beyond the topic of auditory neuroscience, however, as we show that the choice of stimulus scale is an integral part of the neural coding problem and not just a matter of convenience.

Piecewise exponential survival times and analysis of case-cohort data.

Case-cohort designs select a random sample of a cohort to be used as control with cases arising from the follow-up of the cohort. Analyses of case-cohort studies with time-varying exposures that use Cox partial likelihood methods can be computer intensive. We propose a piecewise-exponential approach where Poisson regression model parameters are estimated from a pseudolikelihood and the corresponding variances are derived by applying Taylor linearization methods that are used in survey research. The proposed approach is evaluated using Monte Carlo simulations. An illustration is provided using data from the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study of male smokers in Finland, where a case-cohort study of serum glucose level and pancreatic cancer was analyzed.

Generalized propensity score for estimating the average treatment effect of multiple treatments.

The propensity score method is widely used in clinical studies to estimate the effect of a treatment with two levels on patient's outcomes. However, due to the complexity of many diseases, an effective treatment often involves multiple components. For example, in the practice of Traditional Chinese Medicine (TCM), an effective treatment may include multiple components, e.g. Chinese herbs, acupuncture, and massage therapy. In clinical trials involving TCM, patients could be randomly assigned to either the treatment or control group, but they or their doctors may make different choices about which treatment component to use. As a result, treatment components are not randomly assigned. Rosenbaum and Rubin proposed the propensity score method for binary treatments, and Imbens extended their work to multiple treatments. These authors defined the generalized propensity score as the conditional probability of receiving a particular level of the treatment given the pre-treatment variables. In the present work, we adopted this approach and developed a statistical methodology based on the generalized propensity score in order to estimate treatment effects in the case of multiple treatments. Two methods were discussed and compared: propensity score regression adjustment and propensity score weighting. We used these methods to assess the relative effectiveness of individual treatments in the multiple-treatment IMPACT clinical trial. The results reveal that both methods perform well when the sample size is moderate or large.

Adding local components to global functions for continuous covariates in multivariable regression modeling.

When global techniques, based on fractional polynomials (FPs), are employed for modeling potentially nonlinear effects of several continuous covariates on a response, accessible model equations are obtained. However, local features might be missed. Therefore, a procedure is introduced, which systematically checks model fits, obtained by the multivariable fractional polynomial (MFP) approach, for overlooked local features. Statistically significant local polynomials are then parsimoniously added. This approach, called MFP + L, is seen to result in an effective control of the Type I error with respect to the addition of local components in a small simulation study with univariate and multivariable settings. Prediction performance is compared with that of a penalized regression spline technique. In a setting unfavorable for FPs, the latter outperforms the MFP approach, if there is much information in the data. However, the addition of local features reduces this performance difference. There is only a small detrimental effect in settings where the MFP approach performs better. In an application example with children's respiratory health data, fits from the spline-based approach indicate many local features, but MFP + L adds only few significant features, which seem to have good support in the data. The proposed approach may be expected to be superior in settings with local features, but retains the good properties of the MFP approach in a large number of settings where global functions are sufficient.