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Gerrards Cross, Buckinghamshire, SL9 9RE - 666-888-0000
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- info@tersanpharm.com
- York House Church Lane, Chalfont St Peter,
Gerrards Cross, Buckinghamshire, SL9 9RE - Contact Us
Publications
American Society for Experimental Neurotherapeutics - ASENT 2019 Annual Meeting, 17 July, Maryland, USA
Research in Computational Molecular Biology, Computational Cancer Biology (RECOMB-CCB) 22nd 2018, Paris, France
API (Asociacion Panamericana de Infectologia) - XVIII Congresso Panamericano de Infectologia, 2017, Panama
TAT 2017: 15th International Congress On Targeted Anticancer Therapies, 6-8 Mar 2017, Paris, France
ASTMH 2016: 65th Annual Meeting Of The American Society Of Tropical Medicine And Hygiene, 13-17 Nov 2016, Atlanta, USA
Institut Pasteur International Network Scientific Symposium, 29 Nov-2 Dec 2016, Paris, France
WIN 2014: Breakthrough Biomarker Investigations And Combined Therapeutic Approaches For Precision Cancer Medicine, 23-24 June 2014, Paris, France
EACR-23: 23rd Biennial Congress Of The European Association For Cancer Research. From Basic Research To Personalised Cancer Treatment, 5-8 July 2014, Munich, Germany
19th International Symposium On Hepatitis C Virus And Related Viruses, October 2012, Venice, Italy
18th International Symposium On Hepatitis C Virus And Related Viruses, September 2011, Seattle, Washington USA
The Acute and Prolonged Effects of Different Durations of Foam Rolling on Range of Motion & Stiffness.
by Masatoshi Nakamura, Remi Onuma, Ryosuke Kiyono, Koki Yasaka, Shigeru Sato, (Aug,2018)
DOI: 10.4236/abb.2018.99028
KEM methodology papers
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).
KEM methodology papers
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).
KEM methodology papers
Afshar, M., Dartnell, C., Luzeaux, D., Sallantin, J. & Tognetti, Y. Aristotle’s square revisited to frame discovery science. J. Comput. 2, 54–66 (2007).
KEM methodology papers
“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.
KEM methodology papers
“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.
Formal Concept Analysis
Dartnell, C., Martin, A., Hagège, H. & Sallantin, J. Human Discovery and Machine Learning. Int. J. Cogn. Informatics Nat. Intell. 2, 55–69 (2008).
Formal Concept Analysis
Sallantin, J., Dartnell, C. & Afshar, M. A pragmatic logic of scientific discovery. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) vol. 4265 LNAI 231–242 (Springer Verlag, 2006).
Formal Concept Analysis
Afshar, M., Lanoue, A. & Sallantin, J. Multiobjective/multicriteria optimization and decision support in drug discovery. in Comprehensive Medicinal Chemistry II vol. 4 767–774 (Elsevier, 2006).
Patient Stratification Using KEM
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).
Patient Stratification Using KEM
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).
Case Study – Tersan Pharmaceuticals Ltd, uniquely positioned to identify strong signal in complex datasets.
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.
Real World Evidence Using KEM
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).
Real World Evidence Using KEM
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).
Real World Evidence Using KEM
Werneck, G. L. et al. Comorbidities increase in-hospital mortality in dengue patients in Brazil. Mem. Inst. Oswaldo Cruz 113, 1–5 (2018).
Blood Biomarkers
Hampel, H. et al. Blood-based systems biology biomarkers for next-generation clinical trials in Alzheimer’s disease. 21, 177–191 (2019).
Blood Biomarkers
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).
Blood Biomarkers
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).
Pharmacokinetics
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).
Genomic Patient Profiling
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).
Genomic Patient Profiling
Rodon, J. et al. Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial. Nat. Med. 25, 751–758 (2019).
Genomic Patient Profiling
Bund, C. et al. An integrated genomic and metabolomic approach for defining survival time in adult oligodendrogliomas patients. Metabolomics 15, 1–11 (2019).
Observational Studies
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).
Observational Studies
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).
Tersan Citations
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).
Tersan Citations
Boniolo, F. et al. Artificial intelligence in early drug discovery enabling precision medicine. Expert Opin. Drug Discov. 16, 991–1007 (2021).
Tersan Citations
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).
Tersan Citations
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).
Tersan itations
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).
From data and science to drug approvals.
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.
Pharmacogenomic Data Submissions
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.