The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.

We present SpotOn, a web server to identify and classify interfacial residues as Hot-Spots (HS) and Null-Spots (NS). SpotON implements a robust algorithm with a demonstrated accuracy of 0.95 and sensitivity of 0.98 on an independent test set. The predictor was developed using an ensemble machine learning approach with up-sampling of the minor class. It was trained on 53 complexes using various features, based on both protein 3D structure and sequence. The SpotOn web interface is freely available at: http://milou.science.uu.nl/services/SPOTON/.

Influenza (flu) is a contagious viral disease, which targets the human respiratory tract and spreads throughout the world each year. Every year, influenza infects around 10% of the world population and between 290,000 and 650,000 people die from it according to the World Health Organization (WHO). Influenza viruses belong to the Orthomyxoviridae family and have a negative sense eight-segment single-stranded RNA genome that encodes 11 different proteins. The only control over influenza seasonal epidemic outbreaks around the world are vaccines, annually updated according to viral strains in circulation, but, because of high rates of mutation and recurrent genetic assortment, new viral strains of influenza are constantly emerging, increasing the likelihood of pandemics. Vaccination effectiveness is limited, calling for new preventive and therapeutic approaches and a better understanding of the virus–host interactions. In particular, grasping the role of influenza non-structural protein 1 (NS1) and related known interactions in the host cell is pivotal to better understand the mechanisms of virus infection and replication, and thus propose more effective antiviral approaches. In this review, we assess the structure of NS1, its dynamics, and multiple functions and interactions, to highlight the central role of this protein in viral biology and its potential use as an effective therapeutic target to tackle seasonal and pandemic influenza.

Background: Inflammatory Bowel Disease (IBD), comprising Crohn’s Disease (CD) and Ulcerative Colitis (UC), is a complex condition with diverse manifestations; recent advances in multi-omics technologies are helping researchers unravel its molecular characteristics to develop targeted treatments. Objective: In this work, we explored one of the largest multi-omics cohorts in Inflammatory Bowel Disease, the Study of a Prospective Adult Research Cohort (SPARC IBD), with the goal of identifying predictive biomarkers for CD and UC and elucidating patient subtypes. Design: We analyzed genomics, transcriptomics (gut biopsy samples), and proteomics (blood plasma) from hundreds of patients from SPARC IBD. We trained a machine learning model that classifies UC vs. CD samples. In parallel, we integrated multi-omics data to unveil patient subgroups in each of the two indications independently and analyzed the molecular phenotypes of these patient subpopulations. Results: The high performance of the model showed that multi-omics signatures are able to discriminate between the two indications. The most predictive features of the model, both known and novel omics signatures for IBD, can potentially be used as diagnostic biomarkers. Patient subgroup analysis in each indication uncovered omics features associated with disease severity in UC patients, and with tissue inflammation in CD patients. This culminates with the observation of two CD subpopulations characterized by distinct inflammation profiles. Conclusion: Our work unveiled potential biomarkers to discriminate between CD and UC and to stratify each population into well-defined subgroups, offering promising avenues for the application of precision medicine strategies.

Background: In cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)–powered informational view that integrates the relevant scientific fields and explores new territories. Results: Here, we show SYNPRED, an interdisciplinary approach that leverages specifically designed ensembles of AI algorithms, as well as links omics and biophysical traits to predict anticancer drug synergy. It uses 5 reference models (Bliss, Highest Single Agent, Loewe, Zero Interaction Potency, and Combination Sensitivity Score), which, coupled with AI algorithms, allowed us to attain the ones with the best predictive performance and pinpoint the most appropriate reference model for synergy prediction, often overlooked in similar studies. By using an independent test set, SYNPRED exhibits state-of-the-art performance metrics either in the classification (accuracy, 0.85; precision, 0.91; recall, 0.90; area under the receiver operating characteristic, 0.80; and F1-score, 0.91) or in the regression models, mainly when using the Combination Sensitivity Score synergy reference model (root mean square error, 11.07; mean squared error, 122.61; Pearson, 0.86; mean absolute error, 7.43; Spearman, 0.87). Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application was constructed, allowing easy access by nonexpert researchers. Conclusions: The performance of SYNPRED rivals that of the existing methods that tackle the same problem, yielding unbiased results trained with one of the most comprehensive datasets available (NCI ALMANAC). The leveraging of different reference models allowed deeper insights into which of them can be more appropriately used for synergy prediction. The Combination Sensitivity Score clearly stood out with improved performance among the full scope of surveyed approaches and synergy reference models. Furthermore, SYNPRED takes a particular focus on data interpretability, which has been in the spotlight lately when using the most advanced AI techniques.