Estimating Potency in High-Throughput Screening Experiments by Maximizing the Rate of Change in Weighted Shannon Entropy

High-Throughput Drug Screen for Potential Combinations With Venetoclax Guides the Treatment of Transformed Follicular Lymphoma

Transformed follicular lymphoma (t-FL) is an aggressive malignancy that is refractory and rapidly progressing with poor prognosis. There is currently no effective treatment. High-throughput screening (HTS) platforms are used to profile the sensitivity or toxicity of hundreds of drug molecules, and this approach is applied to identify potential effective treatments for t-FL. We randomly selected a compound panel from the School of Pharmaceutical Sciences Xiamen University, tested the effects of the panel on the activity of t-FL cell lines using HTS and the CCK-8 assay, and identified compounds showing synergistic anti-proliferative activity with the Bcl-2 inhibitor venetoclax (ABT-199). Bioinformatics tools were used to analyze the potential synergistic mechanisms. The single-concentration compound library demonstrated varying degrees of activity across the t-FL cell lines evaluated, of which the Karpas422 cells were the most sensitive, but it was the cell line with the least synergy with ABT-199. We computationally identified 30 drugs with synergistic effects in all cell lines. Molecularly, we found that the targets of these 30 drugs didn't directly regulate Bcl-2 and identified 13 medications with high evidence value above .9 of coordination with ABT-199, further confirming TP53 may play the largest role in the synergistic effect. Collectively, these findings identified the combined regimens of ABT-199 and further suggested that the mechanism is far from directly targeting Bcl-2, but rather through the regulation and synergistic action of p53 and Bcl-2. This study intended to reveal the best synergistic scheme of ABT-199 through HTS to more quickly inform the treatment of t-FL.

Associations between exposure to cadmium, lead, mercury and mixtures and women's infertility and long-term amenorrhea

BACKGROUND

Cadmium (Cd), lead (Pb), and mercury (Hg) have been shown to exhibit endocrine disrupting properties. Their effects on women's reproductive health, however, remain elusive. Here, we investigated associations between blood concentrations of Pb, Cd, Hg, and their mixture and infertility and long-term amenorrhea in women aged 20-49 years using the US National Health and Nutrition Examination Survey (NHANES) 2013-2018 cross-sectional survey.

METHODS

A total of 1,990 women were included for the analysis of infertility and 1,919 women for long-term amenorrhea. The methods of log-transformation and use of quartiles were used to analyze blood heavy metal concentrations. Statistical differences in the covariates between the outcome groups were evaluated using a chi-squared test for categorical variables and a t-test for continuous variables. Multiple logistic regression models were used to examine the associations.

RESULTS

The blood concentrations of Pb and heavy metal mixtures were significantly higher in ever-infertile women than pregnant women, but the concentrations of Cd and Hg were comparable. After full adjustment, multiple logistic regression analyses revealed a significant and dose-dependent positive association between blood Pb concentrations and women's historical infertility, a negative association between Cd and women's long-term amenorrhea, and no associations between Hg and heavy metal mixture and women's infertility or long-term amenorrhea.

CONCLUSIONS

Our study suggests that exposure to heavy metals exhibit differential associations with history of infertility and amenorrhea, and Pb may adversely impact women's reproduction and heighten the risks of infertility and long-term amenorrhea.

Flexible Fitting of PROTAC Concentration-Response Curves with Changepoint Gaussian Processes

A proteolysis-targeting chimera (PROTAC) is a new technology that marks proteins for degradation in a highly specific manner. During screening, PROTAC compounds are tested in concentration-response (CR) assays to determine their potency, and parameters such as the half-maximal degradation concentration (DC₅₀) are estimated from the fitted CR curves. These parameters are used to rank compounds, with lower DC₅₀ values indicating greater potency. However, PROTAC data often exhibit biphasic and polyphasic relationships, making standard sigmoidal CR models inappropriate. A common solution includes manual omitting of points (the so-called masking step), allowing standard models to be used on the reduced data sets. Due to its manual and subjective nature, masking becomes a costly and nonreproducible procedure. We therefore used a Bayesian changepoint Gaussian processes model that can flexibly fit both nonsigmoidal and sigmoidal CR curves without user input. Parameters such as the DC₅₀, maximum effect D_max, and point of departure (PoD) are estimated from the fitted curves. We then rank compounds based on one or more parameters and propagate the parameter uncertainty into the rankings, enabling us to confidently state if one compound is better than another. Hence, we used a flexible and automated procedure for PROTAC screening experiments. By minimizing subjective decisions, our approach reduces time and cost and ensures reproducibility of the compound-ranking procedure. The code and data are provided on GitHub (https://github.com/elizavetasemenova/gp_concentration_response).

Controlling the Reproducibility of AC50 Estimation during Compound Profiling through Bayesian β-Expectation Tolerance Intervals

During drug discovery, compounds/biologics are screened against biological targets of interest to find drug candidates with the most desirable activity profile. The compounds are tested at multiple concentrations to understand the dose-response relationship, often summarized as AC50 values and used directly in ranking compounds. Differences between compound repeats are inevitable because of experimental noise and/or systematic error; however, it is often desired to detect the latter when it occurs. To address this, the β-expectation tolerance interval is proposed in this article. Besides the classical acceptance criteria on assay performance, based on control compounds (e.g., quality control samples), this metric permits us to compare new estimates against historical estimates of the same study compound. It provides a measure that detects whether observed differences are likely due to systematic error. The challenge here is that limited information is available to build such compound-specific acceptance limits. To this end, we propose the use of Bayesian β-expectation tolerance intervals to validate agreement between replicate potency estimates for individual study compounds. This approach allows the variability of the compound-testing process to be estimated from reference compounds within the assay and used as prior knowledge in the computation of compound-specific intervals as from the first repeat of the compound and then continuously updated as more information is acquired with subsequent repeats. A repeat is then flagged when it is not within limits. Unlike a fixed threshold such as 0.5log, which is often used in practice, this approach identifies unexpected deviations on each compound repeat given the observed variability of the assay.

Quality Control of Quantitative High Throughput Screening Data

Quantitative high throughput screening (qHTS) experiments can generate 1000s of concentration-response profiles to screen compounds for potentially adverse effects. However, potency estimates for a single compound can vary considerably in study designs incorporating multiple concentration-response profiles for each compound. We introduce an automated quality control procedure based on analysis of variance (ANOVA) to identify and filter out compounds with multiple cluster response patterns and improve potency estimation in qHTS assays. Our approach, called Cluster Analysis by Subgroups using ANOVA (CASANOVA), clusters compound-specific response patterns into statistically supported subgroups. Applying CASANOVA to 43 publicly available qHTS data sets, we found that only about 20% of compounds with response values outside of the noise band have single cluster responses. The error rates for incorrectly separating true clusters and incorrectly clumping disparate clusters were both less than 5% in extensive simulation studies. Simulation studies also showed that the bias and variance of concentration at half-maximal response (AC50 ) estimates were usually within 10-fold when using a weighted average approach for potency estimation. In short, CASANOVA effectively sorts out compounds with "inconsistent" response patterns and produces trustworthy AC50 values.

Current nonclinical testing paradigms in support of safe clinical trials: An IQ Consortium DruSafe perspective

The transition from nonclinical to First-in-Human (FIH) testing is one of the most challenging steps in drug development. In response to serious outcomes in a recent Phase 1 trial (sponsored by Bial), IQ Consortium/DruSafe member companies reviewed their nonclinical approach to progress small molecules safely to FIH trials. As a common practice, safety evaluation begins with target selection and continues through iterative in silico and in vitro screening to identify molecules with increased probability of acceptable in vivo safety profiles. High attrition routinely occurs during this phase. In vivo exploratory and pivotal FIH-enabling toxicity studies are then conducted to identify molecules with a favorable benefit-risk profile for humans. The recent serious incident has reemphasized the importance of nonclinical testing plans that are customized to the target, the molecule, and the intended clinical plan. Despite the challenges and inherent risks of transitioning from nonclinical to clinical testing, Phase 1 studies have a remarkably good safety record. Given the rapid scientific evolution of safety evaluation, testing paradigms and regulatory guidance must evolve with emerging science. The authors posit that the practices described herein, together with science-based risk assessment and management, support safe FIH trials while advancing development of important new medicines.