Artificial Intelligence for Clinical Trial Design

Harrer, Stefan; Shah, Pratik; Antony, Bhavna J.; Hu, Jianying

doi:10.1016/j.tips.2019.05.005

Cited by 412 publications

(256 citation statements)

References 51 publications

Supporting

Mentioning

252

Contrasting

Unclassified

Order By: Relevance

“…Unlike the current state-of-the-art embryo classification algorithms, [8,10] deep learning provides full automation and standardization of embryo classification, which are both important for clinical adoption. [25,26] The SHIFRA classifiers, which provide early evaluation of blastulation and implantation potential, are the first step toward the development of a decision-making tool that will provide a personalized, multistep, embryo transfer strategy. [27,28] Namely, given a finite number of embryos obtained from a patient and their assessed quality, this tool will specify the multistep order and timing of embryo transfers (including transfers of multiple embryos), as well as which embryos are to be cryopreserved for subsequent transfers.…”

Section: Discussionmentioning

confidence: 99%

Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep‐Learning

Kan‐Tor

Zabari

Erlich

et al. 2020

Advanced Intelligent Systems

View full text Add to dashboard Cite

During in vitro fertilization (IVF) cycles, multiple mature oocytes are retrieved from the ovary and are fertilized in the lab. The newly generated embryos can be transferred into the uterus on day-3,-4, or-5 of incubation, cryopreserved for subsequent transfers or discarded. Lacking a reliable noninvasive evaluation method of the potential to implant, pregnancy rates can be improved by cotransferring multiple embryos thus introducing health risks that are associated with multiple pregnancies. [1] Hence, the evaluation of embryo quality is required for improving live birth rates while minimizing medical complications and shortening time to pregnancy. [2-6] Machine learning was used for assessing the potential of embryos to blastulate [7,8] and to implant [9-11] based on manually annotated morphological and/or morphokientic features. Deep learning, which offers a powerful toolbox for carrying out automated and standardized classification tasks

show abstract

Section: Discussionmentioning

confidence: 99%

Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep‐Learning

Kan‐Tor

Zabari

Erlich

et al. 2020

Advanced Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…A subset of 15 and 50 equidistant snapshots were collected from the set of 150 snapshots. According to Tab 4. it is observed that −T ∆S calculated on 15 and 50 sample snapshots leads to a similar −T ∆S calculated on the whole set.…”

mentioning

confidence: 59%

MMGB/SA Consensus Estimate of the Binding Free Energy Between the Novel Coronavirus Spike Protein to the Human ACE2 Receptor

Forouzesh

Onufriev

2020

Preprint

View full text Add to dashboard Cite

The ability to estimate protein-protein binding free energy in a computationally efficient via a physics-based approach is beneficial to research focused on the mechanism of viruses binding to their target proteins. Implicit solvation methodology may be particularly useful in the early stages of such research, as it can offer valuable insights into the binding process, quickly. Here we evaluate the potential of the related molecular mechanics generalized Born surface area (MMGB/SA) approach to estimate the binding free energy ΔGbind between the SARS-CoV-2 spike receptor-binding domain and the human ACE2 receptor. The calculations are based on a recent flavor of the generalized Born model, GBNSR6. Two estimates of ΔGbind are performed: one based on standard bondi radii, and the other based on a newly developed set of atomic radii (OPT1), optimized specifically for protein-ligand binding. We take the average of the resulting two ΔGbind values as the consensus estimate. For the well-studied Ras-Raf protein-protein complex, which has similar binding free energy to that of the SARS-CoV-2/ACE2 complex, the consensus ΔGbind = −11.8 ± 1 kcal/mol, vs. experimental −9.7 ± 0.2 kcal/mol.The consensus estimates for the SARS-CoV-2/ACE2 complex is ΔGbind = −9.4 ± 1.5 kcal/mol, which is in near quantitative agreement with experiment (−10.6 kcal/mol). The availability of a conceptually simple MMGB/SA-based protocol for analysis of the SARS-CoV-2 /ACE2 binding may be beneficial in light of the need to move forward fast.

show abstract

“…AI/ML technologies have begun to be deployed within key steps of clinical trial design from study preparation to execution, leading to trial success rate improvement. 8 For instance, IBM Watson has developed a system for Clinical Trial Matching, which uses the large quantity of structured and unstructured patient electronic medical record data and the abundance of available trials to create detailed profiles of clinical findings for the patients to compare to trial eligibility criteria. As the system incorporates all the complex protocol criteria to consider, it eliminates the need to manually sort through and analyze complex enrollment criteria and enables clinicians to optimize their search for clinical trials for an eligible patient or for finding patients eligible for a given trial.…”

Section: Ai/ml For Clinical Trial Outcome Prediction and Designmentioning

confidence: 99%

Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?

Zhavoronkov¹,

Vanhaelen²,

Oprea

2020

Clin Pharma and Therapeutics

108

View full text Add to dashboard Cite

As the field of artificial intelligence and machine learning (AI/ML) for drug discovery is rapidly advancing, we address the question “What is the impact of recent AI/ML trends in the area of Clinical Pharmacology?” We address difficulties and AI/ML developments for target identification, their use in generative chemistry for small molecule drug discovery, and the potential role of AI/ML in clinical trial outcome evaluation. We briefly discuss current trends in the use of AI/ML in health care and the impact of AI/ML context of the daily practice of clinical pharmacologists.

show abstract

Artificial Intelligence for Clinical Trial Design

Cited by 412 publications

References 51 publications

Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep‐Learning

Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep‐Learning

MMGB/SA Consensus Estimate of the Binding Free Energy Between the Novel Coronavirus Spike Protein to the Human ACE2 Receptor

Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?

Contact Info

Product

Resources

About