Abstract:Antiretroviral therapy (ART) serves as a mainstay in treating human immunodeficiency virus (HIV)infection. An HIV patient is traditionally administered the same ART regimen for life, even if his/her viral load has been reduced by several orders of magnitude from the initial viral load. Dose reduction in ART has been clinically explored in a trial-and-error manner to reduce side effects and improve ART sustainability. Using artificial intelligence (AI), we have discovered that drugs and doses inputs can be rela… Show more
“…With the emergence of AI, lots of researchers are taking the help of ML and DL algorithms to determine appropriate drug dosage. For instance, Shen et al [ 122 ] developed an AI-based platform, referred to as AI-PRS, to determine the optimum dose and combinations of drugs to be used for HIV treatment through antiretroviral therapy. AI-PRS is a neural network-driven approach, which relates drug combinations and dosage to efficacy through a parabolic response curve (PRS).…”
Section: Applications Of Artificial Intelligence In Drug Development mentioning
Graphic abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind.
The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...
“…With the emergence of AI, lots of researchers are taking the help of ML and DL algorithms to determine appropriate drug dosage. For instance, Shen et al [ 122 ] developed an AI-based platform, referred to as AI-PRS, to determine the optimum dose and combinations of drugs to be used for HIV treatment through antiretroviral therapy. AI-PRS is a neural network-driven approach, which relates drug combinations and dosage to efficacy through a parabolic response curve (PRS).…”
Section: Applications Of Artificial Intelligence In Drug Development mentioning
Graphic abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind.
The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...
“…This can be insurmountably challenging when considering the fact that a small pool of 12 drugs with each drug studied at 10 dosing levels results in 1 trillion possible drug combinations. To overcome this testing challenge, we have previously harnessed an artificial intelligence (AI)-driven quadratic relationship between drug/dose inputs and efficacy/safety outputs to markedly reduce the number of prospective assays needed to pinpoint the right drugs and doses with potential to improve treatment outcomes [ [118] , [119] , [120] , [121] , [122] , [123] ]. To this end, the patient’s clinical/laboratorial data (baseline and follow-ups) and therapeutic regime (initial and adjusted doses) are constantly analyzed using computational power like quadratic phenotypic optimization platform.…”
Section: Optimizing Antiviral Delivery With Artificial Intelligencementioning
confidence: 99%
“…Likewise, AI has been used in a pilot study to optimize the dose of antiretroviral drugs for treating HIV infection. The AI platform identified that the tenofovir dose could be reduced for the long-term maintenance regimen with no relapse for 33 months [ 121 ]. Fig.…”
Section: Optimizing Antiviral Delivery With Artificial Intelligencementioning
confidence: 99%
“…It needs to be highlighted that achieving drug synergy after designing suitable drug combinations does not guarantee favorable treatment outcomes. In addition, drug synergy has been shown to be time/dose dependent and patient-specific [ [118] , [119] , [120] , [121] , [122] ]. In the context of materials-driven drug therapy, this point is important since drug dosing within a combination regimen may need to be dynamically modulated to sustain treatment optimisation ( Fig.…”
Section: Optimizing Antiviral Delivery With Artificial Intelligencementioning
“…Specifically, positive treatment outcomes were observed in direct ex vivo to clinical validation studies for hematologic cancer (Quadratic Phenotypic Optimization Platform [QPOP]), and the key role of dynamic clinical dosing on efficacy was further validated on an advanced solid cancer patient (CURATE.AI) . In the area of infectious diseases, low‐dose treatment for human immunodeficiency virus (HIV) was shown to mediate undetectable viral loads while opening the doors to possible downstream reductions in treatment complications arising from side effects of high‐dose therapy (CURATE.AI) …”
Section: Accelerating Combination Therapy Development and Optimizatiomentioning
The emergence of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), the virus that led to the COVID‐19 (Coronavirus Disease 2019) pandemic, has resulted in substantial overburdening of healthcare systems as well as an economic crisis on a global scale. This has in turn resulted in widespread efforts to identify suitable therapies to address this aggressive pathogen. Therapeutic antibody and vaccine development are being actively explored, and a phase I clinical trial of mRNA‐1273 which is developed in collaboration between the National Institute of Allergy and Infectious Diseases and Moderna, Inc. is currently underway. Timelines for the broad deployment of a vaccine and antibody therapies have been estimated to be 12–18 months or longer. These are promising approaches that may lead to sustained efficacy in treating COVID‐19. However, its emergence has also led to a large number of clinical trials evaluating drug combinations composed of repurposed therapies. As study results of these combinations continue to be evaluated, there is a need to move beyond traditional drug screening and repurposing by harnessing artificial intelligence (AI) to optimize combination therapy design. This may lead to the rapid identification of regimens that mediate unexpected and markedly enhanced treatment outcomes.
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