Convolutional Neural Networks Enable Highly Accurate and Automated Subvisible Particulate Classification of Biopharmaceuticals.

Quantification of subvisible particles, which are generally defined as those ranging in size from 2 to 100 µm, is important as critical characteristics for biopharmaceutical formulation development. Micro Flow Imaging (MFI) provides quantifiable morphological parameters to study both the size and type of subvisible particles, including proteinaceous particles as well as non-proteinaceous features incl. silicone oil droplets, air bubble droplets, etc., thus enabling quantitative and categorical particle attribute reporting for quality control. However, limitations in routine MFI image analysis can hinder accurate subvisible particle classification. In this work, we custom-built a subvisible particle-aware Convolutional Neural Network, SVNet, which has a very small computational footprint, and achieves comparable performance to prior state-of-art image classification models. SVNet significantly improves upon current standard operating procedures for subvisible particulate assessments as confirmed by thorough real-world validation studies.

Discovery of non-boronic acid Arginase 1 inhibitors through virtual screening and biophysical methods.

Inhibiting Arginase 1 (ARG1), a metalloenzyme that hydrolyzes l-arginine in the urea cycle, has been demonstrated as a promising therapeutic avenue in immuno-oncology through the restoration of suppressed immune response in several types of cancers. Most of the currently reported small molecule inhibitors are boronic acid based. Herein, we report the discovery of non-boronic acid ARG1 inhibitors through virtual screening. Biophysical and biochemical methods were used to experimentally profile the hits while X-ray crystallography confirmed a class of trisubstituted pyrrolidine derivatives as optimizable alternatives for the development of novel classes of immuno-oncology agents targeting this enzyme.

Selection of an Optimal In Vitro Model to Assess P-gp Inhibition: Comparison of Vesicular and Bidirectional Transcellular Transport Inhibition Assays.

The multidrug resistance protein 1 (MDR1) P-glycoprotein (P-gp) is a clinically important transporter. In vitro P-gp inhibition assays have been routinely conducted to predict the potential for clinical drug-drug interactions (DDIs) mediated by P-gp. However, high interlaboratory and intersystem variability of P-gp IC50 data limits accurate prediction of DDIs using static models and decision criteria recommended by regulatory agencies. In this study, we calibrated two in vitro P-gp inhibition models: vesicular uptake of N-methyl-quinidine (NMQ) in MDR1 vesicles and bidirectional transport (BDT) of digoxin in Lilly Laboratories Cell Porcine Kidney 1 cells overexpressing MDR1 (LLC-MDR1) using a total of 48 P-gp inhibitor and noninhibitor drugs and digoxin DDI data from 70 clinical studies. Refined thresholds were derived using receiver operating characteristic analysis, and their predictive performance was compared with the decision frameworks proposed by regulatory agencies and selected reference. Furthermore, the impact of various IC50 calculation methods and nonspecific binding of drugs on DDI prediction was evaluated. Our studies suggest that the concentration of inhibitor based on highest approved dose dissolved in 250 ml divided by IC50(I2/IC50) is sufficient to predict P-gp related intestinal DDIs. IC50 obtained from vesicular inhibition assay with a refined threshold of I2/IC50 ≥ 25.9 provides comparable predictive power over those measured by net secretory flux and efflux ratio in LLC-MDR1 cells. We therefore recommend vesicular P-gp inhibition as our preferred method given its simplicity, lower variability, higher assay throughput, and more direct estimation of in vitro kinetic parameters, rather than BDT assay. SIGNIFICANCE STATEMENT: This study has conducted comprehensive calibration of two in vitro P-gp inhibition models: uptake in MDR1 vesicles and bidirectional transport in LLC-MDR1 cell monolayers to predict DDIs. This study suggests that IC50s obtained from vesicular inhibition with a refined threshold of I2/IC50 ≥ 25.9 provide comparable predictive power over those in LLC-MDR1 cells. Therefore, vesicular P-gp inhibition is recommended as the preferred method given its simplicity, lower variability, higher assay throughput, and more direct estimation of in vitro kinetic parameters.

A mass spectrometry-based proteome map of drug action in lung cancer cell lines.

Mass spectrometry-based discovery proteomics is an essential tool for the proximal readout of cellular drug action. Here, we apply a robust proteomic workflow to rapidly profile the proteomes of five lung cancer cell lines in response to more than 50 drugs. Integration of millions of quantitative protein-drug associations substantially improved the mechanism of action (MoA) deconvolution of single compounds. For example, MoA specificity increased after removal of proteins that frequently responded to drugs and the aggregation of proteome changes across cell lines resolved compound effects on proteostasis. We leveraged these findings to demonstrate efficient target identification of chemical protein degraders. Aggregating drug response across cell lines also revealed that one-quarter of compounds modulated the abundance of one of their known protein targets. Finally, the proteomic data led us to discover that inhibition of mitochondrial function is an off-target mechanism of the MAP2K1/2 inhibitor PD184352 and that the ALK inhibitor ceritinib modulates autophagy.

Publisher Correction: A mass spectrometry-based proteome map of drug action in lung cancer cell lines.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Nearest Neighbor Gaussian Process for Quantitative Structure-Activity Relationships.

While Gaussian process models are typically restricted to smaller data sets, we propose a variation which extends its applicability to the larger data sets common in the industrial drug discovery space, making it relatively novel in the quantitative structure-activity relationship (QSAR) field. By incorporating locality-sensitive hashing for fast nearest neighbor searches, the nearest neighbor Gaussian process model makes predictions with time complexity that is sub-linear with the sample size. The model can be efficiently built, permitting rapid updates to prevent degradation as new data is collected. Given its small number of hyperparameters, it is robust against overfitting and generalizes about as well as other common QSAR models. Like the usual Gaussian process model, it natively produces principled and well-calibrated uncertainty estimates on its predictions. We compare this new model with implementations of random forest, light gradient boosting, and k-nearest neighbors to highlight these promising advantages. The code for the nearest neighbor Gaussian process is available at https://github.com/Merck/nngp.

Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure-Activity Relationship Models?

Given a particular descriptor/method combination, some quantitative structure-activity relationship (QSAR) datasets are very predictive by random-split cross-validation while others are not. Recent literature in modelability suggests that the limiting issue for predictivity is in the data, not the QSAR methodology, and the limits are due to activity cliffs. Here, we investigate, on in-house data, the relative usefulness of experimental error, distribution of the activities, and activity cliff metrics in determining how predictive a dataset is likely to be. We include unmodified in-house datasets, datasets that should be perfectly predictive based only on the chemical structure, datasets where the distribution of activities is manipulated, and datasets that include a known amount of added noise. We find that activity cliff metrics determine predictivity better than the other metrics we investigated, whatever the type of dataset, consistent with the modelability literature. However, such metrics cannot distinguish real activity cliffs due to large uncertainties in the activities. We also show that a number of modern QSAR methods, and some alternative descriptors, are equally bad at predicting the activities of compounds on activity cliffs, consistent with the assumptions behind "modelability." Finally, we relate time-split predictivity with random-split predictivity and show that different coverages of chemical space are at least as important as uncertainty in activity and/or activity cliffs in limiting predictivity.

Deep Dive into Machine Learning Models for Protein Engineering.

Protein redesign and engineering has become an important task in pharmaceutical research and development. Recent advances in technology have enabled efficient protein redesign by mimicking natural evolutionary mutation, selection, and amplification steps in the laboratory environment. For any given protein, the number of possible mutations is astronomical. It is impractical to synthesize all sequences or even to investigate all functionally interesting variants. Recently, there has been an increased interest in using machine learning to assist protein redesign, since prediction models can be used to virtually screen a large number of novel sequences. However, many state-of-the-art machine learning models, especially deep learning models, have not been extensively explored. Moreover, only a small selection of protein sequence descriptors has been considered. In this work, the performance of prediction models built using an array of machine learning methods and protein descriptor types, including two novel, single amino acid descriptors and one structure-based three-dimensional descriptor, is benchmarked. The predictions were evaluated on a diverse collection of public and proprietary data sets, using a variety of evaluation metrics. The results of this comparison suggest that Convolution Neural Network models built with amino acid property descriptors are the most widely applicable to the types of protein redesign problems faced in the pharmaceutical industry.

Can Galactose Be Converted to Glucose in HepG2 Cells? Improving the in Vitro Mitochondrial Toxicity Assay for the Assessment of Drug Induced Liver Injury.

Human hepatocellular carcinoma cells, HepG2, are often used for drug mediated mitochondrial toxicity assessments. Glucose in HepG2 culture media is replaced by galactose to reveal drug-induced mitochondrial toxicity as a marked shift of drug IC50 values for the reduction of cellular ATP. It has been postulated that galactose sensitizes HepG2 mitochondria by the additional ATP consumption demand in the Leloir pathway. However, our NMR metabolomics analysis of HepG2 cells and culture media showed very limited galactose metabolism. To clarify the role of galactose in HepG2 cellular metabolism, U-13C6-galactose or U-13C6-glucose was added to HepG2 culture media to help specifically track the metabolism of those two sugars. Conversion to U-13C3-lactate was hardly detected when HepG2 cells were incubated with U-13C6-galactose, while an abundance of U-13C3-lactate was produced when HepG2 cells were incubated with U-13C6-glucose. In the absence of glucose, HepG2 cells increased glutamine consumption as a bioenergetics source. The requirement of additional glutamine almost matched the amount of glucose needed to maintain a similar level of cellular ATP in HepG2 cells. This improved understanding of galactose and glutamine metabolism in HepG2 cells helped optimize the ATP-based mitochondrial toxicity assay. The modified assay showed 96% sensitivity and 97% specificity in correctly discriminating compounds known to cause mitochondrial toxicity from those with prior evidence of not being mitochondrial toxicants. The greatest significance of the modified assay was its improved sensitivity in detecting the inhibition of mitochondrial fatty acid ß-oxidation (FAO) when glutamine was withheld. Use of this improved assay for an empirical prediction of the likely contribution of mitochondrial toxicity to human DILI (drug induced liver injury) was attempted. According to testing of 65 DILI positive compounds representing numerous mechanisms of DILI together with 55 DILI negative compounds, the overall prediction of mitochondrial mechanism-related DILI showed 25% sensitivity and 95% specificity.

Building Quantitative Structure-Activity Relationship Models Using Bayesian Additive Regression Trees.

Quantitative structure-activity relationship (QSAR) is a very commonly used technique for predicting the biological activity of a molecule using information contained in the molecular descriptors. The large number of compounds and descriptors and the sparseness of descriptors pose important challenges to traditional statistical methods and machine learning (ML) algorithms (such as random forest (RF)) used in this field. Recently, Bayesian Additive Regression Trees (BART), a flexible Bayesian nonparametric regression approach, has been demonstrated to be competitive with widely used ML approaches. Instead of only focusing on accurate point estimation, BART is formulated entirely in a hierarchical Bayesian modeling framework, allowing one to also quantify uncertainties and hence to provide both point and interval estimation for a variety of quantities of interest. We studied BART as a model builder for QSAR and demonstrated that the approach tends to have predictive performance comparable to RF. More importantly, we investigated BART's natural capability to analyze truncated (or qualified) data, generate interval estimates for molecular activities as well as descriptor importance, and conduct model diagnosis, which could not be easily handled through other approaches.