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Hadil Faris Alotaibi on Muck Rack

Hadil Faris Alotaibi

As seen in: Nature, MDPI, ACS Publications, Flipboard
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Articles

Sodium Alginate Nanogels Encapsulating Cholecalciferol (Vitamin D3): A Topical Therapeutic Approach Targeting LOX IMVI and A431 Skin Cancer Cells Click to copy article link Article link copied!

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By Hadil Faris Alotaibi, Mustafa A Al-Qadhi
| ACS Publications
1. Introduction Click to copy section linkSection link copied! Skin cancer represents one of the most prevalent malignancies globally, encompassing basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma. Among these, melanoma remains the most lethal due to its high metastatic potential.
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Utilization of advanced machine learning models for analysis of pharmaceutical cocrystals by prediction of solubility parameters using Dragonfly algorithm optimization

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By Hadil Faris Alotaibi, Tareq Nayef AlRamadneh, Anupam Yadav, Ahmed Shakir, T. K. Ramachandran, Swati Mishra, Navin Kumar Tailor, Divya Singhal, Mahendihasan S. Heera
| Nature Verified
Abstract Pharmaceutical cocrystals have emerged as an effective strategy for enhancing the solubility and bioavailability of poorly water-soluble drugs. The successful design of cocrystals largely depends on the selection of suitable coformers, which requires an understanding of the molecular interactions governing cocrystal formation during the process.
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Machine learning–based prediction of paracetamol solubility and CO₂ density in supercritical systems using artificial rabbits optimization

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By Hadil Faris Alotaibi, Junainah Abd Hamid, Subbulakshmi Ganesan, Aman Shankhyan, Rajashree Panigrahi, Fadhil Faez Sead, Aashna Sinha
| Nature Verified
Abstract The accurate prediction of solubility and solvent properties in supercritical CO₂ systems remains a critical challenge in pharmaceutical process design due to the nonlinear and coupled effects of temperature and pressure. This study proposes a novel artificial intelligence-based modeling framework for predicting solvent density and paracetamol mole fraction under supercritical conditions using temperature and pressure as input variables.
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