A precision medicine framework using artificial intelligence for the identification and confirmation of genomic biomarkers of response to an Alzheimer's disease therapy: Analysis of the blarcamesine (ANAVEX2‐73) Phase 2a clinical study

Hampel, Harald; Williams, Coralie; Etcheto, Adrien; Goodsaid, Federico; Parmentier, Frédéric; Sallantin, Jean; Kaufmann, Walter E.; Missling, Christopher U.; Afshar, Mohammad

doi:10.1002/trc2.12013

Cited by 51 publications

(46 citation statements)

References 58 publications

(52 reference statements)

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“…Multiple gastric cancer trials excluded patients with a history of cancer from the eligible cohort. In contrast, some trials did not exclude or mention this group of patients 7,9,10,29,30 . We found that prior cancer exerted completely different impacts on the survival prognosis depending on the different prior cancer types.…”

Section: Discussionmentioning

confidence: 66%

The impact of a prior malignancy on outcomes in gastric cancer patients

Bian

Wang

et al. 2021

Cancer Medicine

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Background The number of cancer survivors has increased rapidly, and there is a higher risk of developing a second cancer. Whether a prior malignancy could affect survival outcomes is unknown. We aimed to investigate the clinical characteristics and prognostic outcomes of prior malignancies in patients with gastric cancer. Methods Patient data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We used the Kaplan–Meier method, competing risk models, and Cox regression models to evaluate the impact of prior malignancy on survival outcomes. Results Among 71,809 patients with primary gastric cancer, 6667 (9.3%) patients had a pre‐existing cancer. Prostate (31.86%), breast (14.34%), and colon and rectum (10.32%) cancer were the most common types. A significant difference was observed in the overall survival rates between patients with and without prior cancer (log‐rank=139.73, p < 0.001). In the subgroup analysis, patients with prostate, uterine corpus, lung and bronchus, colon and rectum, esophagus, urinary bladder, leukemia, brain and other nervous system, oral cavity and pharynx, and breast cancer faced inferior survival than those without prior cancer. Conclusions A history of prior cancer was associated with worse overall survival in patients with gastric cancer, and the effects varied by different initial cancer types. The exclusion and inclusion of patients who had previous malignancies should be reconsidered according to the specific malignancy types.

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Section: Discussionmentioning

confidence: 66%

The impact of a prior malignancy on outcomes in gastric cancer patients

Bian

Wang

et al. 2021

Cancer Medicine

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“…AI/ML approaches have also been applied to drug response studies to treat AD patients in a more precise, personalized way. Hampel et al 429 has built an AI/ML‐based precision medicine framework for identifying the genomic biomarkers of response to AD therapy. Specifically, they studied blarcamesine (ANAVEX2‐73), a selective sigma‐1 receptor agonist, in a Phase 2a trial, where they obtained the patients' whole‐exome and transcriptome data and recorded the measures of safety, clinical features, pharmacokinetics, and efficacy.…”

Section: Ai/ml Applications In Cns Drug Discoverymentioning

confidence: 99%

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Vatansever

Schlessinger

Wacker

et al. 2020

Medicinal Research Reviews

182

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Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML‐driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML‐powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state‐of‐the‐art of AI/ML‐guided CNS drug discovery, focusing on blood–brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

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“…The second is that an effective biomarker must be defined during clinical trials in order to adjust the most effective dose. For instance, Hampel et al [161] recently described that patients with S1R wildtype phenotype and high blarcamesine plasma concentration after IV administration showed the best stabilization of ADCS-ADL and MMSE scores after 162 weeks of barcamesine. No association with more classical biomarkers has been proposed yet.…”

Section: Expert Opinionmentioning

confidence: 99%

Bi-phasic dose response in the preclinical and clinical developments of sigma-1 receptor ligands for the treatment of neurodegenerative disorders

Maurice

2020

Expert Opinion on Drug Discovery

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Introduction: The sigma-1 receptor (S1R) is attracting much attention as a target for diseasemodifying therapies in neurodegenerative diseases. It is a highly conserved protein, present in plasma and endoplasmic reticulum (ER) membranes and enriched in mitochondria-associated ER membranes (MAMs). It modulates ER-mitochondria Ca 2+ transfer and activation of the ER stress pathways. Mitochondrial and MAM dysfunctions contribute to neurodegenerative processes in pathologies such as Alzheimer's disease, Parkinson's disease, Huntington's disease or Amyotrophic Lateral Sclerosis. Interestingly, the S1R can be activated by small druggable molecules and accumulating preclinical data suggest that S1R agonists are effective protectants in these neurodegenerative diseases. Area covered: In this review, we will present the data showing the high therapeutic potential of S1R drugs for the treatment of neurodegenerative diseases, focusing on pridopidine as a potent and selective S1R agonist under clinical development. Of particular interest is the biphasic (bell-shaped) dose-response effect, representing a common feature of all S1R agonists and described in numerous preclinical models in vitro, in vivo and in clinical trials. Expert opinion: S1R agonists modulate essential inter-organelles communication altered in all neurodegenerative diseases and activate numerous intracellular survival pathways. Research in the field will continue growing in the near future. The particular cellular nature of this unique chaperone protein must be better understood to facilitate the developement of promising molecules at the clinical stage.

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A precision medicine framework using artificial intelligence for the identification and confirmation of genomic biomarkers of response to an Alzheimer's disease therapy: Analysis of the blarcamesine (ANAVEX2‐73) Phase 2a clinical study

Cited by 51 publications

References 58 publications

The impact of a prior malignancy on outcomes in gastric cancer patients

The impact of a prior malignancy on outcomes in gastric cancer patients

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Bi-phasic dose response in the preclinical and clinical developments of sigma-1 receptor ligands for the treatment of neurodegenerative disorders

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