Application of Covalent Binding Body Burden in the HµREL Human Hepatocyte Coculture Model for Reactivity Risk Assessment of Metabolically Low Turnover Drugs.

The human hepatocyte suspension model has been a valuable tool to study covalent binding (CVB) for compounds that form reactive metabolites. However, accurately measuring CVB values with the suspension model becomes challenging for metabolically low turnover compounds. In this study, we evaluated the HµREL human hepatocyte coculture model relative to existing literature using human hepatocyte suspension for drugs of known drug-induced liver injury category. Our results indicate that this coculture model provides ample metabolic turnover to reproducibly measure CVB. It is sufficiently robust to apply a predefined 1 mg/day CVB body burden threshold for risk assessment to guide our discovery programs, allowing for expanded coverage to include metabolically low turnover compounds.

Cloud security in a bioanalytical world: considerations for use of third-party cloud services for bioanalysis.

While using the cloud environment for various functions has become commonplace, relatively little attention has been given to considerations for the use of third-party cloud services for regulated bioanalytical workflow and data management. Little guidance has been provided as to how to utilize the cloud to support bioanalytical activities. It can be intimidating when considering how to go about using cloud services for data acquisition, but there are some general ideas to keep in mind when evaluating ways to accommodate regulated bioanalysis online. Determining how to incorporate the use of cloud storage with data that are generated from regulated bioanalytical analysis is an important step in maintaining the security of the data.

Survey Outcome on Immunogenicity Risk Assessment Tools for Biotherapeutics: an Insight into Consensus on Methods, Application, and Utility in Drug Development.

A survey conducted by the Therapeutic Product Immunogenicity (TPI) community within the American Association of Pharmaceutical Scientists (AAPS) posed questions to the participants on their immunogenicity risk assessment strategies prior to clinical development. The survey was conducted in 2 phases spanning 5 years, and queried information about in silico algorithms and in vitro assay formats for immunogenicity risk assessments and how the data were used to inform early developability effort in discovery, chemistry, manufacturing and control (CMC), and non-clinical stages of development. The key findings representing the trends from a majority of the participants included the use of high throughput in silico algorithms, human immune cell-based assays, and proteomics based outputs, as well as specialized assays when therapeutic mechanism of action could impact risk assessment. Additional insights into the CMC-related risks could also be gathered with the same tools to inform future process development and de-risk critical quality attributes with uncertain and unknown risks. The use of the outputs beyond supporting early development activities was also noted with participants utilizing the risk assessments to drive their clinical strategy and streamline bioanalysis.

AI/ML Models to Predict the Severity of Drug-Induced Liver Injury for Small Molecules.

Drug-induced liver injury (DILI), believed to be a multifactorial toxicity, has been a leading cause of attrition of small molecules during discovery, clinical development, and postmarketing. Identification of DILI risk early reduces the costs and cycle times associated with drug development. In recent years, several groups have reported predictive models that use physicochemical properties or in vitro and in vivo assay endpoints; however, these approaches have not accounted for liver-expressed proteins and drug molecules. To address this gap, we have developed an integrated artificial intelligence/machine learning (AI/ML) model to predict DILI severity for small molecules using a combination of physicochemical properties and off-target interactions predicted in silico. We compiled a data set of 603 diverse compounds from public databases. Among them, 164 were categorized as Most DILI (M-DILI), 245 as Less DILI (L-DILI), and 194 as No DILI (N-DILI) by the FDA. Six machine learning methods were used to create a consensus model for predicting the DILI potential. These methods include k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), Naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), weighted average ensemble learning (WA) and penalized logistic regression (PLR). Among the analyzed ML methods, SVM, RF, LR, WA, and PLR identified M-DILI and N-DILI compounds, achieving a receiver operating characteristic area under the curve of 0.88, sensitivity of 0.73, and specificity of 0.9. Approximately 43 off-targets, along with physicochemical properties (fsp3, log S, basicity, reactive functional groups, and predicted metabolites), were identified as significant factors in distinguishing between M-DILI and N-DILI compounds. The key off-targets that we identified include: PTGS1, PTGS2, SLC22A12, PPARγ, RXRA, CYP2C9, AKR1C3, MGLL, RET, AR, and ABCC4. The present AI/ML computational approach therefore demonstrates that the integration of physicochemical properties and predicted on- and off-target biological interactions can significantly improve DILI predictivity compared to chemical properties alone.

Acyl Glucuronide and Coenzyme A Thioester Metabolites of Carboxylic Acid-Containing Drug Molecules: Layering Chemistry with Reactive Metabolism and Toxicology.

Glucuronidation and CoA (coenzyme A) conjugation are common pathways for the elimination of carboxylic acid-containing drug molecules. In some instances, these biotransformations have been associated with toxicity (such as idiosyncratic hepatic injury, renal impairment, hemolytic anemia, gastrointestinal inflammation, and bladder cancer) attributed to, in part, the propensity of acyl glucuronides and acyl CoA thioesters to covalently modify biological macromolecules such as proteins and DNA. It is to be noted that, while acyl glucuronidation and CoA conjugation are indeed implicated in adverse effects, there are many safe drugs in the market that are cleared by these reactive pathways. It is therefore important that new molecular entities with carboxylic acid groups are evaluated for toxicity in a manner that is not unreasonably risk-averse. In the absence of truly predictable methods, therefore, the general approach is to apply a set of end points to generate a weight-of-evidence evaluation. In practice, the focus is to identify structural liabilities and provide structure-activity recommendations early in the program, at a stage where an attempt to improve reactive metabolism does not deoptimize other critical drug-quality criteria. This review will present a high-level overview of the chemistry of glucuronidation and CoA conjugation and provide a discussion of the possible mechanisms of adverse effects that have been associated with these pathways, as well as how such potential hazards are addressed while delivering a new chemical entity for clinical evaluation.

FlatNet3D: intensity and absolute depth from single-shot lensless capture.

Lensless cameras are ultra-thin imaging systems that replace the lens with a thin passive optical mask and computation. Passive mask-based lensless cameras encode depth information in their measurements for a certain depth range. Early works have shown that this encoded depth can be used to perform 3D reconstruction of close-range scenes. However, these approaches for 3D reconstructions are typically optimization based and require strong hand-crafted priors and hundreds of iterations to reconstruct. Moreover, the reconstructions suffer from low resolution, noise, and artifacts. In this work, we propose FlatNet3D-a feed-forward deep network that can estimate both depth and intensity from a single lensless capture. FlatNet3D is an end-to-end trainable deep network that directly reconstructs depth and intensity from a lensless measurement using an efficient physics-based 3D mapping stage and a fully convolutional network. Our algorithm is fast and produces high-quality results, which we validate using both simulated and real scenes captured using PhlatCam.

Determinants of treatment interruption and outcome among smear-positive pulmonary tuberculosis patients in a tuberculosis unit of Purba Bardhaman district of West Bengal.

Context: The adoption of directly observed treatment short course (DOTS) in Revised National Tuberculosis Control Programme has given impressive results with higher treatment success. But interruption of treatment has been one of the major obstacles to treatment of tuberculosis. Aims: The aim of the study was to evaluate the determinants of treatment interruption and outcome. It also evaluated the impact of treatment interruption on treatment outcomes. Settings and Design: The study was carried out in the area covered under Bhatar tuberculosis unit (TU) of Burdwan district of West Bengal. The study was a descriptive cross-sectional study. Methods and Material: Smear-positive pulmonary tuberculosis patients were taken as study subjects in both the components of study. Complete enumeration technique, rather than sampling, was followed in this study. Data were collected in a predesigned and pretested schedule. Statistical Analysis Used: Data were analyzed by percentages and proportion. Chi-square test was used to find the association between variables. Results: Gender, religion, and substance abuse were found to be statistically significant factors with interruption. It was also observed that treatment outcome was not statistically significant with age, gender, religion, and category of treatment. In the study, 84.6% of the study subjects with interruption less than 1 week had favorable outcome. But in study subjects with longer duration of interruption (≥2 weeks), only 12.5% had favorable outcome. Not only interruption, duration of interruption was found to be adversely affecting the treatment outcome. Conclusions: Interruption had an impact on the treatment outcome in the present study. Unfavorable outcomes were significantly more frequent among patients with interruption as compared to those without any interruption.

Driving to a Better Understanding of Acyl Glucuronide Transformations Using NMR and Molecular Modeling.

Acyl glucuronide (AG) metabolites of carboxylic acid-containing drugs and products of their transformations have long been implicated in drug-induced liver injury (DILI). To inform on the DILI risk arising from AG reactive intermediates, a comprehensive mechanistic study of enzyme-independent AG rearrangements using nuclear magnetic resonance (NMR) and density functional theory (DFT) was undertaken. NMR spectroscopy was utilized for structure elucidation and kinetics measurements of nine rearrangement and hydrolysis products of 1ß-O-acyl glucuronide of ibufenac. To extract rate constants of rearrangement, mutarotation, and hydrolysis from kinetic data, 11 different kinetic models were examined. Model selection and estimated rate constant verification were supported by measurements of H/D kinetic isotope effects. DFT calculations of ground and transition states supported the proposed kinetic mechanisms and helped to explain the unusually fast intramolecular transacylation rates found for some of the intermediates. The findings of the current study reinforce the notion that the short half-life of parent AG and slow hydrolysis rates of AG rearrangement products are the two key factors that can influence the in vivo toxicity of AGs.

Toward Unaligned Guided Thermal Super-Resolution.

Thermography is a useful imaging technique as it works well in poor visibility conditions. High-resolution thermal imaging sensors are usually expensive and this limits the general applicability of such imaging systems. Many thermal cameras are accompanied by a high-resolution visible-range camera, which can be used as a guide to super-resolve the low-resolution thermal images. However, the thermal and visible images form a stereo pair and the difference in their spectral range makes it very challenging to pixel-wise align the two images. The existing guided super-resolution (GSR) methods are based on aligned image pairs and hence are not appropriate for this task. In this paper, we attempt to remove the necessity of pixel-to-pixel alignment for GSR by proposing two models: the first one employs a correlation-based feature-alignment loss to reduce the misalignment in the feature-space itself and the second model includes a misalignment-map estimation block as a part of an end-to-end framework that adequately aligns the input images for performing guided super-resolution. We conduct multiple experiments to compare our methods with existing state-of-the-art single and guided super-resolution techniques and show that our models are better suited for the task of unaligned guided super-resolution from very low-resolution thermal images.

FlatNet: Towards Photorealistic Scene Reconstruction From Lensless Measurements.

Lensless imaging has emerged as a potential solution towards realizing ultra-miniature cameras by eschewing the bulky lens in a traditional camera. Without a focusing lens, the lensless cameras rely on computational algorithms to recover the scenes from multiplexed measurements. However, the current iterative-optimization-based reconstruction algorithms produce noisier and perceptually poorer images. In this work, we propose a non-iterative deep learning-based reconstruction approach that results in orders of magnitude improvement in image quality for lensless reconstructions. Our approach, called FlatNet, lays down a framework for reconstructing high-quality photorealistic images from mask-based lensless cameras, where the camera's forward model formulation is known. FlatNet consists of two stages: (1) an inversion stage that maps the measurement into a space of intermediate reconstruction by learning parameters within the forward model formulation, and (2) a perceptual enhancement stage that improves the perceptual quality of this intermediate reconstruction. These stages are trained together in an end-to-end manner. We show high-quality reconstructions by performing extensive experiments on real and challenging scenes using two different types of lensless prototypes: one which uses a separable forward model and another, which uses a more general non-separable cropped-convolution model. Our end-to-end approach is fast, produces photorealistic reconstructions, and is easy to adopt for other mask-based lensless cameras.

Cloud solutions for GxP laboratories: considerations for data storage.

Challenges for data storage during drug development have become increasingly complex as the pharmaceutical industry expands in an environment that requires on-demand availability of data and resources for users across the globe. While the efficiency and relative low cost of cloud services have become increasingly attractive, hesitancy toward the use of cloud services has decreased and there has been a significant shift toward real-world implementation. Within GxP laboratories, the considerations for cloud storage of data include data integrity and security, as well as access control and usage for users around the globe. In this review, challenges and considerations when using cloud storage options for the storage of laboratory-based GxP data are discussed and best practices are defined.

The Evolution of Single-Cell Analysis and Utility in Drug Development.

This review provides a brief history of the advances of cellular analysis tools focusing on instrumentation, detection probes, and data analysis tools. The interplay of technological advancement and a deeper understanding of cellular biology are emphasized. The relevance of this topic to drug development is that the evaluation of cellular biomarkers has become a critical component of the development strategy for novel immune therapies, cell therapies, gene therapies, antiviral therapies, and vaccines. Moreover, recent technological advances in single-cell analysis are providing more robust cellular measurements and thus accelerating the advancement of novel therapies.Graphical abstract.

Invention of MK-8262, a Cholesteryl Ester Transfer Protein (CETP) Inhibitor Backup to Anacetrapib with Best-in-Class Properties.

Cholesteryl ester transfer protein (CETP) represents one of the key regulators of the homeostasis of lipid particles, including high-density lipoprotein (HDL) and low-density lipoprotein (LDL) particles. Epidemiological evidence correlates increased HDL and decreased LDL to coronary heart disease (CHD) risk reduction. This relationship is consistent with a clinical outcomes trial of a CETP inhibitor (anacetrapib) combined with standard of care (statin), which led to a 9% additional risk reduction compared to standard of care alone. We discuss here the discovery of MK-8262, a CETP inhibitor with the potential for being the best-in-class molecule. Novel in vitro and in vivo paradigms were integrated to drug discovery to guide optimization informed by a critical understanding of key clinical adverse effect profiles. We present preclinical and clinical evidence of MK-8262 safety and efficacy by means of HDL increase and LDL reduction as biomarkers for reduced CHD risk.

Whole genome analysis of more than 10 000 SARS-CoV-2 virus unveils global genetic diversity and target region of NSP6.

Whole genome analysis of SARS-CoV-2 is important to identify its genetic diversity. Moreover, accurate detection of SARS-CoV-2 is required for its correct diagnosis. To address these, first we have analysed publicly available 10 664 complete or near-complete SARS-CoV-2 genomes of 73 countries globally to find mutation points in the coding regions as substitution, deletion, insertion and single nucleotide polymorphism (SNP) globally and country wise. In this regard, multiple sequence alignment is performed in the presence of reference sequence from NCBI. Once the alignment is done, a consensus sequence is build to analyse each genomic sequence to identify the unique mutation points as substitutions, deletions, insertions and SNPs globally, thereby resulting in 7209, 11700, 119 and 53 such mutation points respectively. Second, in such categories, unique mutations for individual countries are determined with respect to other 72 countries. In case of India, unique 385, 867, 1 and 11 substitutions, deletions, insertions and SNPs are present in 566 SARS-CoV-2 genomes while 458, 1343, 8 and 52 mutation points in such categories are common with other countries. In majority (above 10%) of virus population, the most frequent and common mutation points between global excluding India and India are L37F, P323L, F506L, S507G, D614G and Q57H in NSP6, RdRp, Exon, Spike and ORF3a respectively. While for India, the other most frequent mutation points are T1198K, A97V, T315N and P13L in NSP3, RdRp, Spike and ORF8 respectively. These mutations are further visualised in protein structures and phylogenetic analysis has been done to show the diversity in virus genomes. Third, a web application is provided for searching mutation points globally and country wise. Finally, we have identified the potential conserved region as target that belongs to the coding region of ORF1ab, specifically to the NSP6 gene. Subsequently, we have provided the primers and probes using that conserved region so that it can be used for detecting SARS-CoV-2. Contact:indrajit@nitttrkol.ac.inSupplementary information: Supplementary data are available at http://www.nitttrkol.ac.in/indrajit/projects/COVID-Mutation-10K.

Harnessing Multi-View Perspective of Light Fields for Low-Light Imaging.

Light Field (LF) offers unique advantages such as post-capture refocusing and depth estimation, but low-light conditions severely limit these capabilities. To restore low-light LFs we should harness the geometric cues present in different LF views, which is not possible using single-frame low-light enhancement techniques. We propose a deep neural network L3Fnet for Low-Light Light Field (L3F) restoration, which not only performs visual enhancement of each LF view but also preserves the epipolar geometry across views. We achieve this by adopting a two-stage architecture for L3Fnet. Stage-I looks at all the LF views to encode the LF geometry. This encoded information is then used in Stage-II to reconstruct each LF view. To facilitate learning-based techniques for low-light LF imaging, we collected a comprehensive LF dataset of various scenes. For each scene, we captured four LFs, one with near-optimal exposure and ISO settings and the others at different levels of low-light conditions varying from low to extreme low-light settings. The effectiveness of the proposed L3Fnet is supported by both visual and numerical comparisons on this dataset. To further analyze the performance of low-light restoration methods, we also propose the L3F-wild dataset that contains LF captured late at night with almost zero lux values. No ground truth is available in this dataset. To perform well on the L3F-wild dataset, any method must adapt to the light level of the captured scene. To do this we use a pre-processing block that makes L3Fnet robust to various degrees of low-light conditions. Lastly, we show that L3Fnet can also be used for low-light enhancement of single-frame images, despite it being engineered for LF data. We do so by converting the single-frame DSLR image into a form suitable to L3Fnet, which we call as pseudo-LF. Our code and dataset is available for download at https://mohitlamba94.github.io/L3Fnet/.

Cholesteryl ester transfer protein (CETP) inhibitors based on cyclic urea, bicyclic urea and bicyclic sulfamide cores.

Cholesteryl ester transfer protein (CETP) inhibitors reduce the transfer of cholesteryl esters from the high-density lipoprotein (HDL-C) to apolipoprotein such as VLDL/LDL, with exchange of triglycerides. Thus, this inhibition increases the HDL-C levels, which is believed to lower the risk for heart disease and stroke. We report here a series of CETP inhibitors based on the cyclic, bicyclic urea and sulfamide cores. These CETP inhibitors exemplified by 15, 31, and 45 demonstrated in vitro potency in inhibiting the CETP transfer activity, and 15, 31 showing in vivo efficacy to increase HDL-C levels in cynomolgus-CETP transgenic mice. The synthesis and biological evaluations of these CETP inhibitors are described.

Inferring the genetic variability in Indian SARS-CoV-2 genomes using consensus of multiple sequence alignment techniques.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a threat to the human population and has created a worldwide pandemic. Daily thousands of people are getting affected by the SARS-CoV-2 virus; India being no exception. In this situation, there is no doubt that vaccine is the primary prevention strategy to contain the wave of COVID-19 pandemic. In this regard, genome-wide analysis of SARS-CoV-2 is important to understand its genetic variability. This has motivated us to analyse 566 Indian SARS-CoV-2 sequences using multiple sequence alignment techniques viz. ClustalW, MUSCLE, ClustalO and MAFFT to align and subsequently identify the lists of mutations as substitution, deletion, insertion and SNP. Thereafter, a consensus of these results, called as Consensus Multiple Sequence Alignment (CMSA), is prepared to have the final list of mutations so that the advantages of all four alignment techniques can be preserved. The analysis shows 767, 2025 and 54 unique substitutions, deletions and SNPs in Indian SARS-CoV-2 genomes. More precisely, out of 54 SNPs, 4 SNPs are present close to the 60% of the virus population. The results of this experiment can be useful for virus classification, designing and defining the dose of vaccine for the Indian population.

Novel advances in biotransformation and bioactivation research-2019 year in review.

Biotransformation is one of the main mechanisms used by the body to eliminate drugs. As drug molecules become more complicated, the involvement of drug metabolizing enzymes increases beyond those that are typically studied, such as the cytochrome P450 enzymes. In this review, we try to capture the many outstanding articles that were published in the past year in the field of biotransformation (see Table 1). We have divided the articles into two categories of (1) metabolites and drug metabolizing enzymes, and (2) bioactivation and safety. This annual review is the fifth of its kind since 2016 (Baillie et al. 2016; Khojasteh et al. 2017, 2018, 2019). This effort in itself also continues to evolve. We have followed the same format we used in previous years in terms of the selection of articles and the authoring of each section. I am pleased of the continued support of Rietjens, Miller, Zhang, Driscoll and Mitra to this review. We would like to welcome Klarissa D. Jackson as a new author for this year's issue. We strive to maintain a balance of authors from academic and industry settings. We would be pleased to hear your opinions of our commentary, and we extend an invitation to anyone who would like to contribute to a future edition of this review. Cyrus Khojasteh, on behalf of the authors.