Browsing by Author "Faheem Patwekar"

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Harnessing artificial intelligence for enhanced Parkinson’s disease management: Pathways, treatment, and prospects
(EnPress Publisher, LLC, 2023) Mohsina Patwekar; Faheem Patwekar; Syed Sanaullah; Daniyal Shaikh; Ustad Almas; Rohit Sharma
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by the accumulation of misfolded proteins and impaired protein degradation mechanisms. Dysregulation of protein degradation processes, including autophagy and the ubiquitin-proteasome system, has been implicated in the pathogenesis of PD. Recently, artificial intelligence (AI) has emerged as a powerful tool to enhance our understanding of protein degradation in PD. This abstract provides an overview of the advancements in studying protein degradation in PD with the aid of AI. The integration of AI techniques, such as machine learning and data mining, has enabled the identification and characterization of protein degradation pathways involved in PD. By analyzing large-scale protein-protein interaction networks, AI algorithms have revealed key interactions and pathways underlying protein degradation dysfunction in PD. Furthermore, AI models can predict the efficiency of protein degradation processes and identify potential targets for enhancing protein degradation in PD, aiding in the development of novel therapeutic interventions.AI-based approaches have also been instrumental in drug discovery and target identification, as they can screen vast databases of compounds to identify potential drugs or small molecules that modulate protein degradation pathways relevant to PD. Additionally, deep learning algorithms have facilitated the analysis of protein structures, predicting protein stability and folding patterns that impact protein degradation. Moreover, AI has played a crucial role in the identification of protein biomarkers associated with protein degradation dysfunction in PD. These biomarkers can aid in early diagnosis and monitoring of the disease, enabling timely intervention and personalized treatment strategies. The advancements presented in this abstract highlight the transformative potential of AI in elucidating the intricate mechanisms of protein degradation in PD. Collaborations between AI researchers, biologists, and clinicians are essential to translate these findings into effective diagnostic tools and therapeutic interventions for PD patients. © 2023 by author(s).
Mechanistic insights on anticancer drugs with specific biological targets and signalling pathways
(Open Exploration Publishing Inc, 2023) Mohsina Patwekar; Faheem Patwekar; Anuradha Medikeri; Shaikh Daniyal; Mohammad A. Kamal; Gulzar Ahmed Rather; Rohit Sharma
Complex enzyme interactions play a role in the spread of cancer, a process fueled by unregulated cell proliferation. DNA topoisomerases, which are important for fixing DNA topological problems, have drawn a lot of interest as potential targets for anti-cancer medications. Cancer treatment, which includes radiation, surgery, and chemotherapy, tries to control cell survival, demise, and mobility, which are mediated by ion transportation across cell membranes via channels and carriers. The malignant transition is characterised by altered channels and carriers. Chemoresistance, which commonly develops after chemotherapy, denotes decreased therapeutic effectiveness against cancer progression. Chemosensitizers are used in combination with anti-cancer medications to overcome this resistance, particularly against adenosine triphosphate (ATP)-binding cassette (ABC) transporters including P-glycoprotein, multidrug resistance-associated protein 1 (MRP1), breast cancer resistance protein (BCRP). Effective targets for treatment are transcription factors, which play a key role in the development of cancer. With the use of interactions with receptors, enzymes, ion channels, transporters, and TFs, nanotechnology improves the safety of tumour localization, treatment, and diagnostics. As a result of mutations or altered signalling, rat sarcoma (RAS) proteins regulate signalling, which is essential for both healthy growth and the development of cancer. Rational treatments that target RAS pathways have the potential to inhibit the growth and spread of tumours. New treatments are still being developed, and they are showing promise in clinical settings. The roles of receptors on tumour cells, their significance for cancer therapy, and recent advancements in preclinical and clinical research are all included in this overview. © The Author(s) 2023.
Navigating the Alzheimer's Treatment Landscape: Unraveling Amyloid-beta Complexities and Pioneering Precision Medicine Approaches
(Bentham Science Publishers, 2024) Mohsina Patwekar; Faheem Patwekar; Shahzad Khan; Rohit Sharma; Dileep Kumar
A variety of cutting-edge methods and good knowledge of the illness's complex causes are causing a sea change in the field of Alzheimer's Disease (A.D.) research and treatment. Precision medicine is at the vanguard of this change, where individualized treatment plans based on genetic and biomarker profiles give a ray of hope for customized therapeutics. Combination therapies are becoming increasingly popular as a way to address the multifaceted pathology of Alzheimer's by simultaneously attacking Aβ plaques, tau tangles, neuroinflammation, and other factors. The article covers several therapeutic design efforts, including BACE inhibitors, gamma-secretase modulators, monoclonal antibodies (e.g., Aducanumab and Lecanemab), and anti-Aβ vaccinations. While these techniques appear promising, clinical development faces safety concerns and uneven efficacy. To address the complicated Aβ pathology in Alzheimer's disease, a multimodal approach is necessary. The statement emphasizes the continued importance of clinical trials in addressing safety and efficacy concerns. Looking ahead, it suggests that future treatments may take into account genetic and biomarker traits in order to provide more personalized care. Therapies targeting Aβ, tau tangles, neuroinflammation, and novel drug delivery modalities are planned. Nanoparticles and gene therapies are only two examples of novel drug delivery methods that have the potential to deliver treatments more effectively, with fewer side effects, and with better therapeutic results. In addition, medicines that target tau proteins in addition to Aβ are in the works. Early intervention, based on precise biomarkers, is a linchpin of Alzheimer's care, emphasizing the critical need for detecting the disease at its earliest stages. Lifestyle interventions, encompassing diet, exercise, cognitive training, and social engagement, are emerging as key components in the fight against cognitive decline. Data analytics and art are gaining prominence as strategies to mitigate the brain's inflammatory responses. To pool knowledge and resources in the fight against Alzheimer's, international cooperation between scientists, doctors, and pharmaceutical companies is still essential. In essence, a complex, individualized, and collaborative strategy will characterize Alzheimer's research and therapy in the future. Despite obstacles, these encouraging possibilities show the ongoing commitment of the scientific and medical communities to combat A.D. head-on, providing a glimmer of hope to the countless people and families touched by this savage sickness. © 2024 Bentham Science Publishers.
Receptor-based approaches and therapeutic targets in Alzheimer’s disease along with role of AI in drug designing: Unraveling pathologies and advancing treatment strategies
(EnPress Publisher, LLC, 2023) Mohsina Patwekar; Faheem Patwekar; Daniyal Shaikh; Shaikh Rohin Fatema; Sunil J. Aher; Rohit Sharma
Alzheimer’s disease (AD) is a prevalent cause of dementia in the elderly, characterized by progressive cognitive decline and neurodegeneration. This review focuses on the etiology of AD, the role of various receptors [TNF (Tumor necrosis factor) receptor, nAChR (Neuronal nicotinic acetylcholine receptors), NMDARs (N-Methyl-D-aspartate receptors), APOE (Apolipoprotein E) receptor, and amyloid-beta receptor], and risk factors contributing to its development. AD progresses through mild, moderate, and severe stages, each exhibiting distinct symptoms. The hallmark pathologies are neurofibrillary tangles and amyloid plaques, comprised of hyperphosphorylated tau protein and amyloid-beta peptides, respectively. Current pharmacotherapeutic options alleviate symptoms but lack a complete cure. To address the challenges in developing effective AD treatments, researchers have turned to artificial intelligence (AI) and computational approaches in drug design. AI techniques, including machine learning and molecular docking, enable the analysis of large datasets and prediction of molecular interactions between potential drug candidates and target receptors. Virtual screening and molecular modeling aid in identifying novel therapeutic compounds. Predictive modeling and optimization algorithms optimize drug properties and predict efficacy. AI also facilitates the repurposing of existing drugs by analyzing their interactions with AD-related receptors and pathways. Clinical trial optimization using AI algorithms enhances patient selection, treatment monitoring, and outcome prediction. Integrating AI into AD drug design holds tremendous promise for accelerating the discovery of effective interventions. By leveraging AI’s capabilities, researchers can efficiently analyze extensive data, predict drug-target interactions, and optimize drug properties, leading to the identification of novel AD treatments. However, further research and validation are needed to translate AI-driven drug design approaches into clinically viable solutions for AD patients. Through continued advancements in AI and collaborative efforts, the development of targeted and advanced therapies for AD is within reach. © 2023 by author(s).