Artificial Intelligence Effecting a Paradigm Shift in Drug Development

Rashid, Masturah Bte Mohd Abdul

doi:10.1177/2472630320956931

Cited by 28 publications

(13 citation statements)

References 71 publications

(77 reference statements)

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“…In drug development, for a new drug to reach the final step and get approved, an estimated $2.8 billion has been spent, and between 10 and 15 years of research were necessary [ 1 , 2 ]. This is due to the fact that most drug candidates fail before reaching the last step of the process, with recent estimates pointing to a success rate of only 2% [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Designing optimized drug candidates with Generative Adversarial Network

et al. 2022

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Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this process. Deep Learning models are emerging as a promising solution to perform de novo drug design, i.e., to generate drug-like molecules tailored to specific needs. However, stereochemistry was not explicitly considered in the generated molecules, which is inevitable in targeted-oriented molecules. This paper proposes a framework based on Feedback Generative Adversarial Network (GAN) that includes optimization strategy by incorporating Encoder–Decoder, GAN, and Predictor deep models interconnected with a feedback loop. The Encoder–Decoder converts the string notations of molecules into latent space vectors, effectively creating a new type of molecular representation. At the same time, the GAN can learn and replicate the training data distribution and, therefore, generate new compounds. The feedback loop is designed to incorporate and evaluate the generated molecules according to the multiobjective desired property at every epoch of training to ensure a steady shift of the generated distribution towards the space of the targeted properties. Moreover, to develop a more precise set of molecules, we also incorporate a multiobjective optimization selection technique based on a non-dominated sorting genetic algorithm. The results demonstrate that the proposed framework can generate realistic, novel molecules that span the chemical space. The proposed Encoder–Decoder model correctly reconstructs 99% of the datasets, including stereochemical information. The model’s ability to find uncharted regions of the chemical space was successfully shown by optimizing the unbiased GAN to generate molecules with a high binding affinity to the Kappa Opioid and Adenosine $$A_ {2a}$$ A 2 a receptor. Furthermore, the generated compounds exhibit high internal and external diversity levels 0.88 and 0.94, respectively, and uniqueness.

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Section: Introductionmentioning

confidence: 99%

Designing optimized drug candidates with Generative Adversarial Network

et al. 2022

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“…Despite high expenditure and more time investment, companies are making investments in research of new drugs for the regulatory approvals, but only a few drugs get approval [32]. So, the use of AI accelerate and facilitate the development of new drugs, leading to a cheaper, more efficient process led to the successful conduct of clinical trials [33]. A study showed statistical improvement in decision-making, the quality of the overall decision-making level, patient satisfaction, and functional outcome in an AI-based randomized clinical trial [34].…”

Section: Clinical Trialmentioning

confidence: 99%

Health in Digital World: A Regulatory Overview in United States

Mishra

Gowrav

Balamuralidhara

et al. 2021

JPRI

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Adaptation towards digitalization in pharmaceuticals leads to the utilization and development of Artificial Intelligence (AI). Significantly it is reducing human workload with the help of an algorithm. Already AI is acting as a key in clinical trial, health care, quality management, manufacturing, product development, and management. Top pharmaceutical companies have adopted AI in different applications within the pharma sector. Different AI models like Machine learning, Artificial Neural Network, Deep Learning, robotics, and Natural Language Processing are being used in pharmaceuticals and healthcare systems. The Worldwide AI market is growing remarkably with a compound annual growth rate of 49.6% and is expected to reach $18,119 million by 2025. So, for better regulation, concerning safety, privacy regulatory strategy is heading towards a better framework. Different regulatory authorities like China, Europe, and United States (US) have adopted AI for economic and policy aspects. Emerging countries are using these tools for administrative work. US has begun implementing frameworks for AI adaptation, research, and development. The AI policy strategy started in 2016 with a series of workshops conducted under the Obama administration. Federal Food and Drug Administration (FDA) has also published draft guidance for regulatory oversight of AI and Machine Learning. In 2021 FDA published a draft regulation for software as a medical device. This review article provides a snapshot of AI implementation in pharmaceuticals and health care with the regulatory approach in the US.

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“…Computational approaches have facilitated the discovery of novel therapeutic targets against cancers in the era of big data. 9 , 25 , 26 , 27 Leveraging on such computational platforms can therefore facilitate the process of target prioritization and early drug discovery. Here, we employ the use of the quadratic phenotypic optimization platform (QPOP) to characterize and narrow down effective combinations of targets which mediate the best possible treatment outcomes in MYC‐driven hepatocellular carcinoma (HCC) as our disease model of choice.…”

Section: Introductionmentioning

confidence: 99%

Splice‐switch oligonucleotide‐based combinatorial platform prioritizes synthetic lethal targets CHK1 and BRD4 against MYC‐driven hepatocellular carcinoma

Thng

Toh

Pigini

et al. 2022

Bioengineering & Transla Med

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Deregulation of MYC is among the most frequent oncogenic drivers in hepatocellular carcinoma (HCC). Unfortunately, the clinical success of MYC‐targeted therapies is limited. Synthetic lethality offers an alternative therapeutic strategy by leveraging on vulnerabilities in tumors with MYC deregulation. While several synthetic lethal targets of MYC have been identified in HCC, the need to prioritize targets with the greatest therapeutic potential has been unmet. Here, we demonstrate that by pairing splice‐switch oligonucleotide (SSO) technologies with our phenotypic‐analytical hybrid multidrug interrogation platform, quadratic phenotypic optimization platform (QPOP), we can disrupt the functional expression of these targets in specific combinatorial tests to rapidly determine target–target interactions and rank synthetic lethality targets. Our SSO‐QPOP analyses revealed that simultaneous attenuation of CHK1 and BRD4 function is an effective combination specific in MYC‐deregulated HCC, successfully suppressing HCC progression in vitro. Pharmacological inhibitors of CHK1 and BRD4 further demonstrated its translational value by exhibiting synergistic interactions in patient‐derived xenograft organoid models of HCC harboring high levels of MYC deregulation. Collectively, our work demonstrates the capacity of SSO‐QPOP as a target prioritization tool in the drug development pipeline, as well as the therapeutic potential of CHK1 and BRD4 in MYC‐driven HCC.

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Artificial Intelligence Effecting a Paradigm Shift in Drug Development

Cited by 28 publications

References 71 publications

Designing optimized drug candidates with Generative Adversarial Network

Designing optimized drug candidates with Generative Adversarial Network

Health in Digital World: A Regulatory Overview in United States

Splice‐switch oligonucleotide‐based combinatorial platform prioritizes synthetic lethal targets CHK1 and BRD4 against MYC‐driven hepatocellular carcinoma

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