Evidentiary standards for validation of multifactorial algorithms in Software as a Medical Device (SaMD) diagnostic and predictive signatures.
Evidentiary standards for validation of multifactorial algorithms in Software as a Medical Device (SaMD) diagnostic and predictive signatures.
From Aducanumab to Tofersen, a tale of clinical development and regulatory discussions based on the power of surrogate endpoints. Two years ago1, on June 7, 2021, the FDA announced the approval of the Biogen therapy aducanumab for the treatment of Alzheimer’s disease.
DOI: 10.4236/abb.2018.99028
What does the FDA expect in 2023 for the submission of pharmacogenomic data as part of INDs or NDAs? In 2023, DNA chips have been replaced by DNA and RNA sequencing. In 2023, drug metabolizing enzyme pharmacogenomics now coexist with pharmacogenomic biomarkers across clinical areas, diseases, therapies and platforms.
DOI: 10.4236/abb.2018.99028
Vatansever, S. et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Medicinal Research Reviews vol. 41 1427–1473 (2021).
DOI: 10.4236/abb.2018.99028
Boniolo, F. et al. Artificial intelligence in early drug discovery enabling precision medicine. Expert Opin. Drug Discov. 16, 991–1007 (2021).
DOI: 10.4236/abb.2018.99028
Brunese, L., Mercaldo, F., Reginelli, A. & Santone, A. An ensemble learning approach for brain cancer detection exploiting radiomic features. Comput. Methods Programs Biomed. 185, 105134 (2020).
DOI: 10.4236/abb.2018.99028
Kim, C., Son, Y. & Youm, S. Chronic disease prediction using character-recurrent neural network in the presence of missing information. Appl. Sci. 9, 2170 (2019).
DOI: 10.4236/abb.2018.99028
Vellas, B., Bain, L. J., Touchon, J. & Aisen, P. S. Advancing Alzheimer’s Disease Treatment: Lessons from CTAD 2018. J. Prev. Alzheimer’s Dis. 6, 198–203 (2019).
DOI: 10.4236/abb.2018.99028
Ouss, L. et al. Behavior and interaction imaging at 9 months of age predict autism/intellectual disability in high-risk infants with West syndrome. Transl. Psychiatry 10, (2020).
DOI: 10.4236/abb.2018.99028
Ouss, L. et al. Developmental trajectories of hand movements in typical infants and those at risk of developmental disorders: An observational study of kinematics during the first year of life. Front. Psychol. 9, 1–15 (2018).
DOI: 10.4236/abb.2018.99028
Castro, M. P. et al. Utility of Serial Transcriptomic Analyses to Characterize the Resistome and to Refine Treatment Selection for Metastatic Colon Cancer: Case Report. Clin. Colorectal Cancer 20, 96–99 (2021).
DOI: 10.4236/abb.2018.99028
Rodon, J. et al. Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial. Nat. Med. 25, 751–758 (2019).
DOI: 10.4236/abb.2018.99028
Bund, C. et al. An integrated genomic and metabolomic approach for defining survival time in adult oligodendrogliomas patients. Metabolomics 15, 1–11 (2019).
DOI: 10.4236/abb.2018.99028
Dilly, S. J. et al. Clinical Pharmacokinetics of a Lipid-Based Formulation of Risperidone, VAL401: Analysis of a Single Dose in an Open-Label Trial of Late-Stage Cancer Patients. Eur. J. Drug Metab. Pharmacokinet. 44, 557–565 (2019).
DOI: 10.4236/abb.2018.99028
Hampel, H. et al. Blood-based systems biology biomarkers for next-generation clinical trials in Alzheimer’s disease. 21, 177–191 (2019).
DOI: 10.4236/abb.2018.99028
Shen, Q. et al. A targeted proteomics approach reveals a serum protein signature as diagnostic biomarker for resectable gastric cancer. EBioMedicine 44, 322–333 (2019).
DOI: 10.4236/abb.2018.99028
Macias, A. E. et al. Mortality among hospitalized dengue patients with comorbidities in Mexico, Brazil, and Colombia. Am. J. Trop. Med. Hyg. 105, 102–109 (2021).
DOI: 10.4236/abb.2018.99028
Mereiter, S. et al. The Thomsen-Friedenreich Antigen: A Highly Sensitive and Specific Predictor of Microsatellite Instability in Gastric Cancer. J. Clin. Med. 7, 256 (2018).
DOI: 10.4236/abb.2018.99028
Here is a striking example of how we found key information in a very small population and successfully validated it on a much larger scale. By analyzing a few cases in selected small cities in Brazil, we unearthed crucial associations between diagnoses and operations in Brazil as a whole.
DOI: 10.4236/abb.2018.99028
Maciás, A. E. et al. Real-World Evidence of Dengue Burden on Hospitals in Mexico: Insights From the Automated Subsystem of Hospital Discharges (Saeh) Database. Rev. Investig. Clin. 71, 168–177 (2019).
DOI: 10.4236/abb.2018.99028
Werneck, G. L. et al. Comorbidities increase in-hospital mortality in dengue patients in Brazil. Mem. Inst. Oswaldo Cruz 113, 1–5 (2018).
DOI: 10.4236/abb.2018.99028
Hampel, H. et al. 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. Alzheimer’s Dement. Transl. Res. Clin. Interv. 6, 1–15 (2020).
DOI: 10.4236/abb.2018.99028
Abtroun, L., Bunouf, P., Gendreau, R. M. & Vitton, O. Is the efficacy of milnacipran in fibromyalgia predictable? A data-mining analysis of baseline and outcome variables. Clin. J. Pain 32, 435–440 (2016).
DOI: 10.4236/abb.2018.99028
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nat. 2012 4837391 483, 603–607 (2012).
DOI: 10.4236/abb.2018.99028
Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nat. 2012 4837391 483, 570–575 (2012).
DOI: 10.4236/abb.2018.99028
Metz, J. T. et al. Navigating the kinome. Nat. Chem. Biol. 2011 74 7, 200–202 (2011).
DOI: 10.4236/abb.2018.99028
Keiser, M. J. et al. Predicting new molecular targets for known drugs. Nat. 2009 4627270 462, 175–181 (2009).
Karaman, M. W. et al. A quantitative analysis of kinase inhibitor selectivity. Nat. Biotechnol. 2008 261 26, 127–132 (2008).
DOI: 10.4236/abb.2018.99028
Hampel, H. et al. 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. Alzheimer’s Dement. Transl. Res. Clin. Interv. 6, 1–15 (2020).
DOI: 10.4236/abb.2018.99028
Abtroun, L., Bunouf, P., Gendreau, R. M. & Vitton, O. Is the efficacy of milnacipran in fibromyalgia predictable? A data-mining analysis of baseline and outcome variables. Clin. J. Pain 32, 435–440 (2016).
DOI: 10.4236/abb.2018.99028
Jullian, N., Tognetti, Y. & Afshar, M. Applications of Rule-Based Methods to Data Mining of Polypharmacology Data Sets. Data Min. Drug Discov. 57, 241–256 (2013).
DOI: 10.4236/abb.2018.99028
Jullian, N., Jourdan, N. & Afshar, M. Hypothesis Generation for Scientific Discovery . Examples from the Use of KEM ® , a Rule-Based Method for Multi- Objective Analysis and Optimization. Towar. drugs Futur. key issues lead Find. lead Optim. (2008).
DOI: 10.4236/abb.2018.99028
Afshar, M., Dartnell, C., Luzeaux, D., Sallantin, J. & Tognetti, Y. Aristotle’s square revisited to frame discovery science. J. Comput. 2, 54–66 (2007).
DOI: 10.4236/abb.2018.99028
“Is the Efficacy of Milnacipran in Fibromyalgia Predictable? A Data-Mining Analysis of Baseline and Outcome Variables”, L Abtroun, P Bunouf, RM Gendreau and O Vitton, Clin. J. Pain, 32 (2016) 435–440.
DOI: 10.4236/abb.2018.99028
“Hypothesis Generation for Scientific Discovery. Examples from the Use of KEM®, a Rule-Based Method for Multi-Objective Analysis and Optimization”, Nathalie Jullian, Nathalie Jourdan, Mohammad Afshar, Solvay Pharmaceuticals Conferences, 18 (2008), 75-80.
DOI: 10.4236/abb.2018.99028
Evidentiary standards for validation of multifactorial algorithms in Software as a Medical Device (SaMD) diagnostic and predictive signatures.
From Aducanumab to Tofersen, a tale of clinical development and regulatory discussions based on the power of surrogate endpoints. Two years ago1, on June 7, 2021, the FDA announced the approval of the Biogen therapy aducanumab for the treatment of Alzheimer’s disease.
DOI: 10.4236/abb.2018.99028
What does the FDA expect in 2023 for the submission of pharmacogenomic data as part of INDs or NDAs? In 2023, DNA chips have been replaced by DNA and RNA sequencing. In 2023, drug metabolizing enzyme pharmacogenomics now coexist with pharmacogenomic biomarkers across clinical areas, diseases, therapies and platforms.
DOI: 10.4236/abb.2018.99028