Mario DiPaola

Osteoarthritis (OA) is a leading cause of lameness in horses. A number of treatment modalities are currently available for OA, including NSAIDs, disease modifying systemic drugs such as pentosan polysulfate (PPS), corticosteroids, platelet rich plasma (PRP), autologous conditioned serum (IRAP/ACS), stem cell therapy (MSC), extracellular vesicles (EVs) and amnion products. While NSAIDs and steroids alleviate pain and inflammation temporarily, they have minimal impact on stopping or reversing joint damage. Therapeutics modalities such as PRP, IRAP, MSC, EVs and amnion products are more likely to not only block inflammation but also potentially reverse joint damage. Over the past decade, the use of regenerative therapeutics in veterinary medicine has garnered significant interest. While stem cells represent a familiar approach to induce tissue regeneration and healing ¹ , other therapeutic products such as tissue-derived biologics, as well as purified biologics from natural tissues have shown great promise in the treatment of various diseases and musculoskeletal system injuries ² .

Integrating artificial intelligence (AI) with the Quantitative Structure-Activity Relationship (QSAR) has transformed modern drug discovery by empowering faster, more accurate, and scalable identification of therapeutic compounds. This review outlines the evolution from classical QSAR methods, such as multiple linear regression and partial least squares, to advanced machine learning and deep learning approaches, including graph neural networks and SMILES-based transformers. Molecular docking and molecular dynamics simulations are presented as cooperative tools that boost the mechanistic consideration and structural insight into the ligand-target interactions. Discussions on using PROTACs and targeted protein degradation, ADMET prediction, and public databases and cloud-based platforms to democratize access to computational modeling are well presented with priority. Challenges related to authentication, interpretability, regulatory standards, and ethical concerns are examined, along with emerging patterns in AI-driven drug development. This review is a guideline for using computational models and databases in explainable, data-rich and profound drug discovery pipelines.

Resistance to cancer drugs is a complex phenomenon that poses a significant challenge in the treatment of various malignancies. This review comprehensively explores cancer resistance mechanisms and discusses emerging strategies and modalities to overcome this obstacle. Many factors contribute to cancer resistance, including genetic mutations, activation of alternative signaling pathways, and alterations in the tumor microenvironment. Innovative approaches, such as targeted protein degradation, immunotherapy combinations, precision medicine, and novel drug delivery systems, hold promise for improving treatment outcomes. Understanding the intricacies of cancer resistance and leveraging innovative modalities are essential for advancing cancer therapy.

The persistent threat of cancer remains a significant hurdle for global health, prompting the exploration of innovative approaches in the quest for successful therapeutic interventions. Cyclin-dependent kinase 9 (CDK9), a central player in transcription regulation and cell cycle progression, has emerged as a promising target to combat cancer. Its pivotal role in oncogenic pathways and the pressing need for novel cancer treatments has propelled CDK9 into the spotlight of drug discovery efforts. This article presents a comprehensive study that connects a multidisciplinary approach, combining computational methodologies, experimental validation, and the transformative Proteolysis-Targeting Chimera (PROTAC) technology. By uniting these diverse techniques, we aim to identify, characterize, and optimize a new class of degraders targeting CDK9. We explore these compounds for targeted protein degradation, offering a novel and potentially effective approach to cancer therapy. This cohesive strategy utilizes the combination of computational predictions and experimental insights, with the goal of advancing the development of effective anticancer therapeutics, targeting CDK9.

The persistent threat of cancer remains a significant hurdle for global health, prompting the exploration of innovative approaches in the quest for successful therapeutic interventions. Cyclin-dependent kinase 9 (CDK9), a central player in transcription regulation and cell cycle progression, has emerged as a promising target to combat cancer. Its pivotal role in oncogenic pathways and the pressing need for novel cancer treatments has propelled CDK9 into the spotlight of drug discovery efforts. This article presents a comprehensive study that connects a multidisciplinary approach, combining computational methodologies, experimental validation, and the transformative PROTAC (Proteolysis-Targeting Chimera) technology. By uniting these diverse techniques, we aim to identify, characterize, and optimize a new class of degraders targeting CDK9. We explore these compounds for kinase inhibitory properties and potential to induce targeted protein degradation, offering a novel and potentially more sustainable approach to cancer therapy. This cohesive strategy expresses the interface between computational predictions and experimental insights, with the ultimate goal of advancing the development of effective anticancer therapeutics targeting CDK9.