How can machine learning and multiscale modeling benefit ocular drug development?

Wang, Nannan; Zhang, Yunsen; Wang, Wei; Ye, Zhuyifan; Chen, Hongyu; Hu, Guanghui; Ouyang, Defang

doi:10.1016/j.addr.2023.114772

Cited by 16 publications

(5 citation statements)

References 344 publications

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“…83,84 MIDD be especially valuable for clinical development of rare diseases as it provides a framework that maximizes the inference that can be drawn from all the available evidence. 85 As reviewed by Wang et al, 86 sophisticated methods using datadriven ML and AI have been introduced into the clinical development of ocular drugs. For example, ML and AI techniques have facilitated diagnosis and prediction of ophthalmic diseases, leveraging real-world evidence, clinical trial design, and drawing inference from ocular imaging data (e.g., optical coherence tomography, retinal, and slit-lamp images).…”

Section: Modeling Efforts For Retina Gene Therapymentioning

confidence: 99%

Clinical Pharmacology Perspective on Development of Adeno‐Associated Virus Vector‐Based Retina Gene Therapy

Ford,

Karatza,

Mody

et al. 2024

Clin Pharma and Therapeutics

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Adeno‐associated virus (AAV) vector‐based gene therapy is an innovative modality being increasingly investigated to treat diseases by modifying or replacing defective genes or expressing therapeutic entities. With its unique anatomic and physiological characteristics, the eye constitutes a very attractive target for gene therapy. Specifically, the ocular space is easily accessible and is generally considered “immune‐privileged” with a low risk of systemic side effects following local drug administration. As retina cells have limited cellular turnover, a one‐time gene delivery has the potential to provide long‐term transgene expression. Despite the initial success with voretigene neparvovec (Luxturna), the first approved retina gene therapy, there are still challenges to be overcome for successful clinical development of these products and scientific questions to be answered. The current review paper aims to integrate published experience learned thus far for AAV‐based retina gene therapy related to preclinical to clinical translation; first‐in‐human dose selection; relevant bioanalytical assays and strategies; clinical development considerations including trial design, biodistribution and vector shedding, immunogenicity, transgene expression, and pediatric populations; opportunities for model‐informed drug development; and regulatory perspectives. The information presented herein is intended to serve as a guide to inform the clinical development strategy for retina gene therapy with a focus on clinical pharmacology.

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Section: Modeling Efforts For Retina Gene Therapymentioning

confidence: 99%

Clinical Pharmacology Perspective on Development of Adeno‐Associated Virus Vector‐Based Retina Gene Therapy

Ford,

Karatza,

Mody

et al. 2024

Clin Pharma and Therapeutics

View full text Add to dashboard Cite

show abstract

“…In silico modeling and simulations offer unique advantages by providing detailed insights and facilitating rational formulation design. The integration of in silico methodologies, overcoming data challenges, and interdisciplinary collaborations can lead to more efficient and objective-oriented drug formulation design in the era of Pharma 4.0 [ 145 , 146 , 147 , 148 ].…”

Section: Ai For Drug Deliverymentioning

confidence: 99%

“…AI helps create newer proteins, peptides, nucleic acid biologics and immunotherapeutics [ 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 ]. AI algorithms could help to build proteins and peptides with desired features [ 153 , 154 , 155 , 156 , 157 ].…”

Section: Ai For Drug Deliverymentioning

confidence: 99%

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora,

Gholap,

Jetha

et al. 2023

Pharmaceutics

242

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Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

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Section: Regulatory Standards and Approvalsmentioning

confidence: 99%

“…Regulatory standards and approvals play a crucial role in ensuring the safety and effectiveness of materials used in ophthalmic lenses. Chitosan and starch, when utilized in lens technology, are subject to stringent regulatory scrutiny to meet established safety and quality standards [ 112–117 ] : Food and Drug Administration (FDA) Approval: In the United States, the Food and Drug Administration (FDA) oversees the approval of medical devices, including ophthalmic lenses. Lens materials and coatings containing chitosan and starch must undergo rigorous testing and evaluation to meet FDA standards for safety and efficacy. Conformite Europeene (CE) Marking : In Europe, the CE marking is a certification that indicates conformity with health, safety, and environmental protection standards.…”

Section: Biocompatibility and Safety Considerationsmentioning

confidence: 99%

Revolutionizing Lens Technology: Chitosan and Starch in Next‐Gen Ophthalmic Lenses

Aljibori,

Shaker,

Alamiery

et al. 2023

Starch Stärke

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This comprehensive review explores the transformative impact of chitosan and starch in advancing ophthalmic lens technology. Derived from chitin, chitosan and starch‐based materials stand out for their exceptional biocompatibility, moisture retention, and optical clarity. The synergy between these biopolymers has unlocked new frontiers in lens design and coatings, promising elevated performance. The article scrutinizes their multifaceted applications, ranging from innovative lens materials to advanced coatings, while emphasizing their regulatory approvals and commitment to environmental sustainability. Chitosan and starch emerge as vanguards, ushering in a future marked by enhanced wearer comfort, optical clarity, and ecological responsibility. This review encapsulates their paramount significance, current applications, and exciting potential, portraying them as pioneers in shaping the next generation of state‐of‐the‐art ophthalmic lenses.

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How can machine learning and multiscale modeling benefit ocular drug development?

Cited by 16 publications

References 344 publications

Clinical Pharmacology Perspective on Development of Adeno‐Associated Virus Vector‐Based Retina Gene Therapy

Clinical Pharmacology Perspective on Development of Adeno‐Associated Virus Vector‐Based Retina Gene Therapy

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Revolutionizing Lens Technology: Chitosan and Starch in Next‐Gen Ophthalmic Lenses

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