On a scale of 1 to 5 (1=definitely not feasible; 3=too uncertain to guess, 5= highly feasible), do you think it will be feasible to rely solely on AI- and ML-based toxicity prediction models for safety testing of chemicals, pesticides, pharmaceuticals, and food ingredients within the next decade? (Please feel free to explain)

Answer Explanations

• 1

  user-678105

  My results from Leadscope and In Vitro MultiFlow were incorrect when further analyses were performed.

• 1

  user-297238

  Please see expiation for #1

• 1

  user-528003

  I don't believe that there are enough data to adequately predict in vivo safety based on AI and ML alone.

• 3

  user-711942

  Not likely, there should not be an expectation that something will “always” hold true.

• 4

  user-992364

  Feasible, but only at lower Tiers.

• 3

  user-918734

  Not initially. We will need a parallel model.

• 3

  user-90122

  Again this is incorrectly describing what AI/ML is. Garbarge in, garbage out. Most experiments are not accurate enough and/or do not capture all aspects of natural biology yet (think time resolution of biological effects, circadian rhythm, between human/sample variation, etc).

• 2

  user-602841

  NOt in the next decade - classical toxicity tests will still be needed

• 1

  user-320876

  As I have expressed concerns in my recent articles and books, unless scientists try to better comprehend the complex electrochemical and highly regulated immune neuroplasticity of human body in health or disease processes, applications of AI or ML could additionally produce false flags that are based on false foundations. Given numerous isolated data that are not integrated or understood on potential biological harms of drugs, vaccines, pesticides, GMO foods and ingredients, as well as other potential genotoxins (EMFs, other low-level carcinogens) including diverse individual health status, make AI or ML subject to un-predictable errors and miscalculations.

• 3

  user-483397

  With the seeming acceleration of AI into all fields, 10 years is a long time and I would be hopeful such applications have been tried and at least partially validated.

• 3

  RAR53

  I don’t think the time has come from deliberate exposure of drugs to humans - exposure is too high as is uncertainty of hazard. Foods may be ok depending on the extent of exposure and suitable analog data. I think depending on the application pesticides and other chemistries are more amenable since exposure is often incidental and can be ameliorated with proper PPE.

• 5

  user-821082

  Larger populations are needed to test hypotheses

• 4

  user-542826

  See 1 above

• 1

  user-43697

  As mentioned above, AI/ML today and in the foreseeable future will not be able to predict any of the complex endpoints like repeated (systemic) tox, reprotox, immunotox, (non-genotoxic) carcinogenicity etc. Thus, AI/ML replacing animal testing in the foreseeable future will not be feasible.

• 2

  user-571430

  The first step of screening might profit from AI techniques. It would be a good way of selecting the agents worth further experimental testing. It might also save costs, but it would not replace laboratory experiments.

• 2

  user-321504

  In conjunction with my answer to #1, I believe it is important to use all tools available, including human study design, experimentation, and interpretation.

• 3

  user-200863

  see #1

• 5

  user-441980

  I do believe, it will be highly feasible. Considering the recent rapid growth of AI and ML, I think it will be feasible to rely solely on AI- and ML-based toxicity prediction models for safety testing of chemicals, pesticides, pharmaceuticals, and food ingredients within the next decade, even before.

• 2

  user-91797

  Again, unless NAMs exists that completely and accurately replace animal testing, I do not see how AI and ML can make this transition in 10 years, particularly when the EPA goal is by 2035.

• 1

  user-984892

  Practical aspects is also needed

• 1

  user-598239

  The word "solely" is a deal breaker.

• 1

  user-44510

  Ten years is a too short period to built reliable AI models.

• 2

  user-476126

  the interface between biotic variation and chemical variation will be too much to rely solely on these models.

• 3

  user-74194

  Not solely. AI and ML are tools for gathering information. Interpretation of this new information is a job for humans. Applying this information to regulatory issues with wisdom is also a job for humans.

• 1

  user-604069

  The process needs substantial validation

• 2

  user-998359

  I dont think we have enough QSAR validation for all endpoints to do this in the next decade

• 3

  user-718379

  Still need experts

• 1

  user-125195

  At this time except for certain well defined cases it is not possible to do this alone for the safety testing of chemicals, pesticides, pharmaceuticals and public health and food ingredients in a health protective manner. They can be applied to good effect as supplemental tools but not as stand alone tools

• 4

  user-901200

  With properly conducted in vitro assays, for sure.

• 4

  user-33117

  I do believe that cross-validation against live (at least in vitro) experimentation will only reinforce the belief in AI validation.

• 4

  user-77300

  I hope so

• 1

  user-152010

  For pharmaceuticals the regulations will not allow to rely only AI- and ML-based toxicity prediction models and completely replace animal and human investigations due to lack of confidence and difficulties around explainability of those models, however they could decrease the need considerably for those investigations and save a lot of time and costs.

• 4

  user-847725

  In some use cases, this is feasible, e.g. for chemicals used in industrial use or inertly in consumer products with minimal exposure risk to the general public. For pesticides, food additives, and pharmaceuticals I think some level of safety testing will be needed, but can be prioritized/assisted by AI/ML toxicity predictions.

• 4

  user-986831

  I feel that AI amd ML could be game changers in the future but their output totally depends upon the quality and depth of input data.

• 1

  user-377267

  The acceptance of decisions purely based on AI/ML is low because of the burden of uncertainty. Thus, NAM data (in vitro etc.) are always needed to a certain extend. Even then, the process of gaining acceptance is quite full of obstacles...

• 5

  user-479056

  But applicability domain is not sufficiently addressed.

• 3

  user-908889

  AI and ML will have a place, but we shouldn't entirely remove human sense

• 3

  user-532952

  Food safety variables are multiple, pesticides can have long term effects that only manifest decades later. If AI and ML are used, the model could be rather based on variables and observations of the literature. NOT just recent literature. Some of the best Toxicity research is over 50 years old. Journals only publish NEW research and researchers are encouraged to use references less than 5 years old. Some facts are definitely older than 5 years...

• 2

  user-189445

  The AI- and ML-based toxicity prediction models will be useful tools to concur the development of safety tests, but not within the next ten years

• 1

  user-750501

  The toxicity testing speed and quantity are not sufficient to feed enough data to an AI/ML system to be successful. More data is needed to "teach" AI/ML.

• 2

  user-189305

  We will always need some biological validation

• 3

  user-995929

  We need more reliable models as well for evaluation of the ML model and to supplement.

• 1

  user-726131

  See above.

• 1

  user-508906

  Interpretation of complex data sets will not be possible with AI/ML but it will be an important tool for certain approved analysis.

• 3

  user-900365

  Again, same as the response for question 2 - it depends on the endpoints, some endpoints are more mature than others.

• 1

  user-889807

  Perhaps someday, but not in the near future. Currently, ai/ml would best serve screening and prioritizing chemicals for hazard.

• 3

  user-895875

  AI and ML are at the beginning stage. We must learn how to trust the prediction model. As a result, basic research is still needed.

• 4

  user-874787

  Yes with new and improved models day to day, it would be certainly feasible to have better predictable models

• 1

  user-480186

  See above.