Back to task, my questions about using machines to create and test drugs to be used on humans are:
1) THE OBVIOUS QUESTION ABOUT THIS IS: WHAT MAKES SCIENTIST THINK TESTING A SYNTHETIC DRUG ON A COMPUTER DATABASE IS THE EQUIVALENT OF TESTING IT ON A REAL HUMAN WITH HUMAN CELLS, BLOOD, ANTIBODIES, ALLERGIES ETC?
2) THE SECOND OBVIOUS QUESTION IS: ARE ALL HUMANS WHO VISIT DOCTORS OR ARE ADMITTED TO HOSPITALS GUINEA PIGS WITHOUT THEIR CONSENT? AND ARE THEIR RECORDS FED INTO A I DATABASES TO 'TRAIN AI"WITHOUT HUMANS'CONSENT?
3) THE THIRD OBVIOUS QUESTION IS: DOES A I 'TRAINING'USING MEDICAL RECORDS OF REAL HUMANS WITHOUT THEIR KNOWLEDGE OR CONSENT A VIOLATION OF THE INTERNATIONAL CODE OF LAW LABELED "NUREMBERG CODEOF RESEARCH" THAT PROHIBITS RESEARCH ON HUMANS WITHOUT THEIR CONSENT?
I am including here the research I did quickly after reading news article today, that prompted these questions. Please read below these snippets of news articles, Google searches, and research on this topic .
Quote:
"We mistake thinking AI is a general intelligence; thinking it has human characteristics. It's not. This is mathematics.
''What it doesn't change is what we are as humans and it doesn't change the need for empathy and sympathy and understanding in all our human relationships.'' words of Prof Andrew Hopkins of Wales.
https://www.bbc.com/news/uk-wales-67171042;
about exscientia.ai which is using A I to manipulate molecules to create new drugs faster;
21 Oct 2023; 6am _____________
Ohio State is also using drugs to manipulate molecules to create drugs faster:
Quote: "Our aim was to use AI to accelerate the drug design process, and we found that it not only saves researchers time and money but provides drug candidates that may have much better properties than any molecules that exist in nature.”
... This study builds on previous research of Ning’s where her team developed a method named Modof that was able to generate molecule structures that exhibited desired properties better than any existing molecules...
Having such a dynamic and effective device at scientists’ disposal could enable the industry to manufacture stronger drugs at a quicker pace – but despite the edge AI might give scientists inside the lab, Ning emphasizes the medicines G2Retro or any generative AI creates still need to be validated – a process that involves the created molecules being tested in animal models and later in human trials. “We are very excited about generative AI for medicine, and we are dedicated to using AI responsibly to improve human health,” said Ning.
This research was supported by Ohio State’s President’s Research Excellence Program and the National Science Foundation.
https://news.osu.edu/using-ai-to-create-better-more-potent-medicines/ 21Oct 2023; 6:18am
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Harvard is using AI to create drugs also:
"Milestones in AI-Enabled Drug Discovery
Far from being a distant sci-fi future, AI-enabled drug discovery is already here. A non-exhaustive list of historic milestones in the field includes the following achievements:
* In early 2020, Exscientia announced the first-ever AI-designed drug molecule to enter human clinical trials. * In July 2021, an AI system by DeepMind called AlphaFold predicted the protein structures for 330,000 proteins, including all 20,000 proteins in the human genome. The AlphaFold Protein Structure Database has since expanded to include over 200 million proteins, covering nearly all cataloged proteins known to science.
* In February 2022, Insilico Medicine reported the start of Phase I clinical trials for the first-ever AI-discovered molecule based on an AI-discovered novel target—all done at a fraction of the time and cost of traditional preclinical programs.
* In January 2023, AbSci became the first entity “to create and validate de novo antibodies in silico” using generative AI.
* In February 2023, the FDA granted its first Orphan Drug Designation to a drug discovered and designed using AI; Insilico Medicine plans to begin a global Phase II trial for the drug “early” this year.
According to Boston Consulting Group, as of March 2022, “biotech companies using an AI-first approach [had] more than 150 small-molecule drugs in discovery and more than 15 already in clinical trials.” But how exactly is AI being used to accomplish these milestones, and why does it matter?"
How AI Is Being Used
Traditional drug discovery is a notoriously time consuming and expensive process, with pre-clinical stages typically taking three to six years and costing hundreds of millions to billions of dollars. However, a host of AI tools are revolutionizing nearly every stage of the drug discovery process, offering substantial potential to reshape the speed and economics of the industry.
* Target identification: At the target identification phase of drug discovery, AI is being trained on large datasets, including omics datasets, phenotypic and expression data, disease associations, patents, publications, clinical trials, research grants, and more to understand the biological mechanisms of diseases and to identify novel proteins and/or genes that can be targeted to counteract those diseases. Combined with systems like AlphaFold, AI can go even further than mere target identification by predicting the 3D structures of targets and accelerating the design of appropriate drugs that bind to them.
* Molecular simulations: AI is also being used to reduce the need for physical testing of candidate drug compounds by enabling high-fidelity molecular simulations that can be run entirely on computers (i.e., in silico) without incurring the prohibitive costs of traditional chemistry methods. * Prediction of drug properties: Some AI systems are being used to bypass simulated testing of drug candidates by predicting key properties such as toxicity, bioactivity, and the physicochemical characteristics of molecules.
* De novo drug design: While traditional drug discovery has historically involved the screening of large libraries of candidate molecules, AI is shifting this paradigm too. Some systems are capable of generating promising and never-before-seen drug molecules entirely from scratch.
* Candidate drug prioritization: Once a set of promising “lead” drug compounds has been identified, AI is used to rank these molecules and prioritize them for further assessment, with AI approaches outperforming previous ranking techniques.
* Synthesis pathway generation: Going beyond theoretical drug design, AI is also being used to generate synthesis pathways for producing hypothetical drug compounds, in some cases suggesting modifications to compounds to make them easier to manufacture.
As AI systems continue to improve, the idea of fully automated end-to-end drug discovery appears less and less to be matter of if, but of when....
However, it also opens the floodgates to a host of unresolved issues relating to, e.g., intellectual property rights, the risk of technological misuse, and the continued assurance of drug safety and efficacy in this new era.
https://blog.petrieflom.law.harvard.edu/2023/03/20/how-artificial-intelligence-is-revolutionizing-drug-discovery/;
21 Oct 2023; 6:26am
Answers from @Google Search this morning:
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What is an omics database?
"Omics data is information generated by studies ending with -omics: genomics, proteomics, phenomics, etc. It all started with genomics. When the field of genomics first appeared, it was principally different from genetics since it focused on studying the whole genome rather than single gen"
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Is AI being used in medical research?
"We have already seen many published reports that use AI to interpret images — radiographs, histology, and optic fundi. Tools that utilize AI have come into increasing use in analyzing and interpreting large research databases containing information ranging from laboratory findings to clinical data."
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How is A I being used in drug development?
"Aside from identifying drug targets, AI has also been used successfully in the virtual screening of compounds, such as identifying those that can bind to “undruggable” targets, de novo drug design, drug repurposing and identification of treatment response biomarkers."
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Which pharmaceutical companies use A I in drug trials?
"Companies such as Amgen (AMGN. O), Bayer (BAYGn.DE) and Novartis (NOVN. S) are training AI to scan billions of public health records, prescription data, medical insurance claims and their internal data to find trial patients - in some cases halving the time it takes to sign them up.Sep 22, 2023" Insight: Big Pharma bets on AI to speed up clinical trials | Reutersreuters.com
https://www.reuters.com › technology › big-pharma-bets...
21 Oct 2023; 6:47am ------------------------
1st drug created by A I is in clinical trials
"Hong Kong-based biotech InSilico Medicine has used artificial intelligence (AI) to create the drug INS018-055 to help treat idiopathic pulmonary fibrosis (IPF).
IPF is a disease whereby the tissue surrounding the alveoli in the lungs becomes inflamed and thick, causing scarring within the lungs. This causes the lungs to become increasingly stiff, making it difficult for respiration. Pirfenidone and nintedanib are the two main medications used to help delay the progression of IPF.
Pirfenidone works by suppressing the activity of the immune system, whereas nintedanib works by helping slow down the progression of scarring within the lungs. InSilico’s INS-08055 acts by targeting discoidin domain receptor 1 (DDR1). INS-08055 acts by inhibiting DDR1, a collagen-activated pro-inflammatory receptor tyrosine kinase, expressed in epithelial cells and involved in fibrosis. The drug candidate, by inhibiting DDR1, alleviates the disease condition and is administered through intravenous and oral routes. Unlike other AI-produced drugs in trials, INS018_055 is the first drug with both a novel AI-discovered target and a novel AI-generated design."
https://www.clinicaltrialsarena.com/comment/first-drug-created-ai-enters-trials/:~:text=Unlike%20other%20AI-produced%20drugs,a%20novel%20AI-generated%20design.
21 Oct 2023; 6:53am
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from Google search
What are the disadvantages of AI in healthcare?
* Training complications. ...
* Risk of creating unemployment. ...
* Too much change can be difficult to manage. ...
* Still requires human input. ...
* Increased security risk. ...
* Social variables may not be considered. ...
* Inaccuracies could occur."
* 21 Oct 2023; 6:58am
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