Drug safety assessment by machine learning models.

The evaluation of drug-induced Torsades de pointes (TdP) risks is crucial in drug safety assessment. In this study, we discuss machine learning approaches in the prediction of drug-induced TdP risks using preclinical data. Specifically, a random forest model was trained on the dataset generated by the rabbit ventricular wedge assay. The model prediction performance was measured on 28 drugs from the Comprehensive In Vitro Proarrhythmia Assay initiative. Leave-one-drug-out cross-validation provided an unbiased estimation of model performance. Stratified bootstrap revealed the uncertainty in the asymptotic model prediction. Our study validated the utility of machine learning approaches in predicting drug-induced TdP risks from preclinical data. Our methods can be extended to other preclinical protocols and serve as a supplementary evaluation in drug safety assessment.

Meta-analysis application to hERG safety evaluation in clinical trials.

One objective of meta-analysis, which synthesizes evidence across multiple studies, is to assess the consistency and investigate the heterogeneity across studies. In this project, we performed a meta-analysis on moxifloxacin (positive control in QT assessment studies) data to characterize the exposure-response relationship and determine the safety margin associated with 10-msec QTc effects for moxifloxacin based on 26 thorough QT studies submitted to the FDA. Multiple meta-analysis methods were used (including two novel methods) to evaluate the exposure-response relationship and estimate the critical concentration and the corresponding confidence interval of moxifloxacin associated with a 10-msec QTc effect based on the concentration-QTc models. These meta-analysis methods (aggregate data vs. individual participant data; fixed effect vs. random effect) were compared in terms of their precision and robustness. With the selected meta-analysis method, we demonstrated the homogeneity and heterogeneity of the moxifloxacin concentration-QTc relationship in studies. We also estimated the critical concentration of moxifloxacin that can be used to calculate the hERG safety margin of this drug.

Integrated strategies for robust growth of Chlorella vulgaris on undiluted dairy farm liquid digestate and pollutant removal.

Undiluted dairy farm liquid digestate contains high levels of organic matters, chromaticity and total ammonia nitrogen (TAN), resulting in inhibition to microalgal growth. In this study, a novel cascade pretreatment with ozonation and ammonia stripping (O + S) was employed to remove these inhibitors, and was compared with single pretreatment approach. The optimum parameters for ozonation and ammonia stripping were obtained and the mechanisms of inhibition elimination were investigated. The results show that ozonation contributed to the degradation of non-fluorescent chromophoric organics through the direct molecular ozone attack, which mitigated the inhibition of chromaticity to microalgae, while ammonia stripping relieved the inhibition of high TAN to microalgae. After cascade pretreatment, TAN, total nitrogen (TN), COD and chromaticity were reduced by 80.2 %, 75.4 %, 20.6 % and 75.8 % respectively. When C. vulgaris was cultured on different pretreated digestate, it was found that cascade pretreatment was beneficial for retaining high PSII activity and synergistically improved microalgal growth. The highest biomass increment and productivity achieved 5.40 g L-1 and 900 mg L-1 d-1 respectively in the integration system of cascade pretreatment with microalgae cultivation (O + S + M). After O + S + M treatment, the removal efficiencies of TAN, TN, COD and total phosphorus (TP) were 100 %, 92.8 %, 46.7 % and 99.6 %, respectively. This work provided a promising strategy (O + S + M) for sustainable liquid digestate treatment, along with nutrient recovery and value-added biomass production.

Statistical learning in preclinical drug proarrhythmic assessment.

Torsades de pointes (TdP) is an irregular heart rhythm characterized by faster beat rates and potentially could lead to sudden cardiac death. Much effort has been invested in understanding the drug-induced TdP in preclinical studies. However, a comprehensive statistical learning framework that can accurately predict the drug-induced TdP risk from preclinical data is still lacking. We proposed ordinal logistic regression and ordinal random forest models to predict low-, intermediate-, and high-risk drugs based on datasets generated from two experimental protocols. Leave-one-drug-out cross-validation, stratified bootstrap, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The potential outlier drugs identified by our models are consistent with their descriptions in the literature. Our method is accurate, interpretable, and thus useable as supplemental evidence in the drug safety assessment.

Effects of three surfactants on co-conversion of endogenous carbon and nitrogen of dairy wastewater in mesophilic hydrolytic acidification coupled microalgae culture system.

To determine the feasibility of enhancing co-conversion of endogenous C and N in dairy wastewater by surfactants, the effects of varying dosages of sodium lignosulfonate (LS), sodium dodecyl sulfate (SDS), and Tween-80 on mesophilic hydrolytic acidification coupled microalgae culture system were investigated. Tween-80 had a positive effect on hydrolytic acidification, while LS had no clear effect and SDS had a negative effect. Tween-80 significantly increased the C/N ratio in hydrolytic acidification liquor (HAL) (maximum HAc/NH4+-N rate reached 7.90 in 2.9% Tween-80 test). Pyrosequencing analysis demonstrated that community richness and diversity decreased and the proportion of acidobacteria increased with increasing Tween-80 dosage. Furthermore, the effect of Tween-80-enhanced HAL on microalgae (Chlorella pyrenoidosa) growth and nitrogen removal and the assimilation of C and N on the microalgae culture system were investigated. The biomass concentration and a C and N assimilation yield of 4.8% Tween-80 test (1.75 g/L, 825.01 mg/L, 126.68 mg/L) were significantly higher than those of both the low-concentration Tween-80 tests and control. The yield of protein and carbohydrates and higher heating value in the microalgae culture system were also promoted by Tween-80.

[Chemical constituents from the leaves of Cinnamomum camphora var. linaloolifera and their anti-inflammatory activities].

Thirteen compounds were isolated and purified from the leaves of Cinnamomum camphora by the macroporous resin,silica gel,and Sephadex LH-20 column chromatographies. Those compounds were further identified by IR,UV,MS,and NMR techniques:( 2 S)-1-( 3″,4″-methylenedioxy phenyl)-3-( 2',6'-dimethoxy-4'-hydroxyphenyl)-propan-2-ol( 1),( 2 R,3 R)-5,7-dimethoxy-3',4'-methylenedioxy flavanol( 2),9-hydroxysesamin( 3),sesamin( 4),piperitol( 5),kobusin( 6),(-)-aptosimon( 7),acuminatolide( 8),1ß,11-dihydroxy-5-eudesmene( 9),lasiodiplodin( 10),vanillin( 11),p-hydroxybenzaldehyde( 12),and p-hydroxybenzoic acid ethyl ester( 13). Compound 1 was a novel compound,and compounds 2,6,7,9 and 10 were isolated from Cinnamomum plants for the first time. Compounds 4,7 and 10 were found to possess good inhibitory effect on IL-6 production in LPS-induced BV2 cells at a concentration of 20 µmol·L-1 in the in vitro bioassay,with inhibition rates of 51. 26% ± 4. 13%,67. 82% ± 3. 77% and85. 81%±1. 19%,respectively.

The joint effect of ammonium and pH on the growth of Chlorella vulgaris and ammonium removal in artificial liquid digestate.

Although ammonium containing digestate is an ideal alternative medium for microalgae cultivation, high ammonium or unfavorable pH may inhibit microalgal growth. In this study, the joint effect of ammonium and pH on the growth of C. vulgaris and nutrient removal in artificial digestate was investigated. Our results show that ammonium and pH both affected algal growth, but free ammonia (FA) was the main actual inhibitory factor. Algal specific growth rate presented a negative correlation with FA and their relationship was well fitted by a linear regression model. Microalgal growth was little affected below 36.8 mg L-1 FA, while the obvious inhibition occurred at 184 mg L-1 FA (EC50), indicating a high tolerance to FA. Ammonium removal was well described by a first-order kinetics model. FA stress stimulated the production of extracellular organic matters (EOMs), which was good for microalgae adaptation but adverse to pollutant removal.

Baseline selection in concentration-QTc modeling: impact on assay sensitivity.

The baseline selection in concentration-QTc (C-QTc) modeling is not well studied in the literature. Time-matched baseline and pre-dose baseline have been commonly used as a covariate in C-QTc modeling for parallel and crossover study, respectively. It has been showed that the C-QTc model using time-matched baseline has a low chance of showing assay sensitivity in parallel study. To better understand the impacts of baseline section in C-QTc, we examined the original and subsampled moxifloxacin and placebo data from more than 50 of TQT studies submitted to FDA with regard to assay sensitivity. Our analyses show that baseline selection (time-matched, pre-dose, average) has an impact on prediction from C-QTc modeling and the impact depends on study design (parallel, crossover). The impact to categorical table of ΔQTc is unlikely to alter the interpretation of the outlier category (ΔQTc>60) that corresponds to the regulatory concern. The results presented here can guide C-QTc study design as well as baseline selection in C-QTc modeling.

Detection and impact of hysteresis when evaluating a drug's QTc effect using concentration-QTc analysis.

Early-phase studies quantifying the QTc prolongation potential for a new drug often use linear concentration-QTc (C-QTc) models, assuming no delay between plasma concentrations and QTc changes. However, that assumption is not always correct. The term "hysteresis" has been utilized to describe a time lag present between a measurable concentration and a measurable effect. To detect and quantify hysteresis and its impact on study interpretation, studies with hysteresis of 0.25-4 h were simulated with different doses, half-lives, and sampling schedules in a crossover design. Hysteresis was quantified using a novel method termed exposure-normalized GRI (enGRI), a proposed modification of the Glomb-Ring Index (GRI), to account for delay and magnitude of QTc effects. With realistic sampling, the rate of false negative studies (FN) increased proportionally to the delay, even for delays shorter than 1 h. Using an enGRI threshold (γ) of 2 ms resulted in FN with undetected delay and FN without hysteresis at approximately the same rate. For γ = 2 ms, the specificity of enGRI was > 90% throughout the investigated scenarios. We therefore propose the incorporation of enGRI when interpreting results from C-QTc analysis with the intent of characterizing QTc effects.

Assay sensitivity in "Hybrid thorough QT/QTc (TQT)" study.

A concurrent positive control should be included in a thorough QTc clinical trial to validate the study according to ICH E14 guidance. Some pharmaceutical companies have started to use "hybrid TQT" study to meet ICH E14 regulatory requirements since the release of ICH E14 Q&A (R3). The "hybrid TQT" study includes the same treatment arms (therapeutic and/or supratherapeutic dose of investigational drug, placebo, and positive control) with sample size less than traditional TQT studies, but use concentration-QTc (C-QTc) analysis as primary analysis and assay sensitivity analysis. To better understand the statistical characteristics of assay sensitivity with a commonly used positive control - Moxifloxacin - in "hybrid TQT" studies, we examined the original and subsampled moxifloxacin and placebo data from more than a hundred of TQT studies submitted to FDA. The assay sensitivity results are quite consistent between classical E14 analysis and C-QTc analysis using the original datasets. Performance of assay sensitivity in "hybrid TQT" studies using subsampled data depends on number of moxifloxacin subjects, study design (crossover design and parallel design), and C-QTc model. The results presented here can aid the design of future "hybrid TQT" studies.

Evaluation of dependent variable, time effect, covariates, and covariation structure in concentration-QTc modeling: A simulation study.

The revised ICH E14 Question and Answer (R3) document issued in December 2015 enables pharmaceutical companies to use concentration-QTc (C-QTc) modeling as the primary analysis for assessing QTc prolongation risk of new drugs. A new approach by including the time effect into the current C-QTc model is introduced. Through a simulation study, we evaluated performances of different C-QTc modeling with different dependent variables, covariates, and covariance structures. This simulation study shows that C-QTc models with ΔQTc being dependent variable without time effect inflate false negative rate and that fitting C-QTc models with different dependent variables, covariates, and covariance structures impacts the control of false negative and false positive rates. Appropriate C-QTc modeling strategies with good control of false negative rate and false positive rate are recommended.

Correction to: Scientific white paper on concentration-QTc modeling.

The original version of this article unfortunately contained an error in Equation 1 under the section "Pre-specified linear mixed effects model". The correct equation has given below.

Hydrolysis dynamics for batch anaerobic digestion of elephant grass.

Elephant grass might be a potential source of fine chemical precursors and bioenergy. In the present study, we investigated the dynamics of hydrolysis of elephant grass. Three models were used to fit the hydrolysis rate constants-flat, spherical, and cylindrical models. The hydrolysis rate constants obtained using the spherical model presented the best fit between the experimental and theoretical values. Furthermore, we determined the secondary reinforcement points and interventions that can be introduced to speed up the hydrolysis process. Our findings will provide information for studies on the hydrolysis of elephant grass and promote its application in the biogas industry as an alternative biofuel.

Scientific white paper on concentration-QTc modeling.

The International Council for Harmonisation revised the E14 guideline through the questions and answers process to allow concentration-QTc (C-QTc) modeling to be used as the primary analysis for assessing the QTc interval prolongation risk of new drugs. A well-designed and conducted QTc assessment based on C-QTc modeling in early phase 1 studies can be an alternative approach to a thorough QT study for some drugs to reliably exclude clinically relevant QTc effects. This white paper provides recommendations on how to plan and conduct a definitive QTc assessment of a drug using C-QTc modeling in early phase clinical pharmacology and thorough QT studies. Topics included are: important study design features in a phase 1 study; modeling objectives and approach; exploratory plots; the pre-specified linear mixed effects model; general principles for model development and evaluation; and expectations for modeling analysis plans and reports. The recommendations are based on current best modeling practices, scientific literature and personal experiences of the authors. These recommendations are expected to evolve as their implementation during drug development provides additional data and with advances in analytical methodology.

One-step production of biodiesel from wet and unbroken microalgae biomass using deep eutectic solvent.

One-step and Two-step methods were studied for lipid extraction from wet and unbroken (water content is 65-67%) Chlorella sp. and Chlorococcum sp. (GN38) using deep eutectic solvent (DES) treated microalgae cells. Further we optimized the extraction process and studied on its underlying mechanism. Among all DES, Choline chloride-Acetic acid (Ch-Aa) DES treatment showed optimal conditions at the mass ratio of DES: methanol-H2SO4 (2.00%) mixture: algae biomass was 60:40:3 with reaction time was 60min, and the optimum temperature was 110°C (Chlorococcum sp.) and 130°C (Chlorella sp.) respectively. The total content of FAME by One-step method with DES treatment was improved by 30% compared with Two-step method. This process is effective on wet and unbroken paste of microalgae biomass, so the FAME extracted using one-step with DES process is feasible for microalgae based biodiesel production.