Journal of Drug Research in Ayurvedic Sciences

Register      Login

VOLUME 4 , ISSUE 1 ( January–March, 2019 ) > List of Articles

REVIEW ARTICLE

Pharmacovigilance in Ayurveda: Statistical Input for Signal Detection

Arunabh Tripathi, Rohit Sharma, Rohit K Ravte, Jayram Hazra

Keywords : Logistic regression, Pharmacovigilance, Signal detection,Adverse drug reaction

Citation Information : Tripathi A, Sharma R, Ravte RK, Hazra J. Pharmacovigilance in Ayurveda: Statistical Input for Signal Detection. J Drug Res Ayurvedic Sci 2019; 4 (1):33-38.

DOI: 10.5005/jdras-10059-0061

License: CC BY-NC 4.0

Published Online: 00-03-2019

Copyright Statement:  Copyright © 2019; Jaypee Brothers Medical Publishers (P) Ltd.


Abstract

Aim: To review the intrinsic tenants available for safe drug usage in Ayurveda and to contextualize the statistical signal detection techniques of current times in terms of Ayurvedic pharmacovigilance program. Materials and methods: Streamlining the information to develop a database that differentiates between known adverse drug reactions (ADRs) from hitherto unknown drug reactions per the standard definition of ADR. To introduce amicable statistical methods viz., Chi-square test, odds ratio (OR), and logistic regression for signal detection. Results and conclusion: The proposed method of developing a known ADR and safe drug usage practices described in Ayurveda that followed the application of standard operating procedures for signal detection as per the pharmacovigilance program by applying statistical methods suggested will ensure pragmatic signal detection.


PDF Share
  1. Krishnamurthy KH. A source book of Indian medicine: an anthology. B.R. Pub. Corp; 1991. p. 2 , 9.
  2. Krishnamurthy KH. A source book of Indian medicine: an anthology. B.R. Pub. Corp; 1991. p. 8.
  3. Krishnamurthy KH. A source book of Indian medicine: an anthology. B.R. Pub. Corp; 1991. p. 67.
  4. Ayurveda Industry Market Size, Strength and Way Forward [Internet]. 2018 [cited 2019 Mar 22]. Available from: www.cii.in. p. 5.
  5. Ayurveda Industry Market Size, Strength and Way Forward [Internet]. 2018 [cited 2019 Mar 22]. Available from: www.cii.in. p. 19.
  6. Government of India Ministry of Health and Family Welfare (Department of Health). The Drugs and Cosmetics Act and Rules. The Drugs and Cosmetics Act, 1940 (23 of 1940). The Drugs and Cosmetics Rules, 1945 List of Abbreviations Used [Internet]. [Cited 2019 Mar 28]. Available from: http://www.cdsco.nic.in/writereaddata/Drugs&CosmeticAct.pdf. p. 19.
  7. Government of India Ministry of Health and Family Welfare (Department of Health). The Drugs and Cosmetics Act and Rules. The Drugs and Cosmetics Act, 1940 (23 of 1940). The Drugs and Cosmetics Rules, 1945 List of Abbreviations Used [Internet]. [cited 2019 Mar 28]. Available from: http://www.cdsco.nic.in/writereaddata/Drugs&CosmeticAct.pdf. p. 1, 27.
  8. Edwards RI, Aronson JK. Adverse drug reactions: definitions, diagnosis, and management. The Lancet 2000;356(9237):1255–1259. DOI: 10.1016/S0140-6736(00)02799-9.
  9. Ajanal M, Nayak S, Prasa BS, et al. Adverse drug reaction and concepts of drug safety in ayurveda: an overview. J Young Pharm 2013;5(4):116–120. DOI: 10.1016/j.jyp.2013.10.001.
  10. Chaudhary A, Singh N, Kumar N. Pharmacovigilance, boon for the safety and efficacy of ayuvedic formulations. J Ayurveda Integr Med 2010;1(4):251–256. DOI: 10.4103/0975-9476.74427.
  11. Thatte U, Bhalerao S. Pharmacovigilance of Ayurvedic medicines in India. Indian J Pharmacol 2008;40(Suppl 1):S10–S12.
  12. National pharmacovigilance programme for Ayurveda Sidha and Unani Drugs - 2008 (Protocal).
  13. Delamothe T. Reporting adverse drug reactions. Br Med J 1992;304:465. . Available from [https://www.who-umc.org/research-scientific-development/signal-detection/what-is-a-signal/].
  14. Hauben M, Aronson JK. Defining ‘Signal’ and its subtypes in pharmacovigilance based on a systematic review of previous definitions. Drug Saf 2009;32(2):99–110. DOI: 10.2165/00002018-200932020-00003.
  15. Online Available from [https://www.primevigilance.com/resources/complexities-drug-safety/signal-detection-in-pharmacovigilance/].
  16. Online Available from https://adr.who-umc.org/container.asp?sSessionId=&sPage=.
  17. Kalaiselvan V, Tripathi A, Saurabh A, et al. Quantitative methods for identification of signals for individual case safety reports in India. Ther Innov Regul Sci 2015;49(2):898–902.
  18. Prakash BG, R SK, Chandra Reddy VK, et al. Knowledge, attitude, and practice of pharmacovigilance among ayurvedic practitioners: a questionnaire survey in Andhra Pradesh, India. Natl J Physiol Pharm Pharmacol [Internet] 2016. Available from: www.njppp.com.
  19. TKDL Traditional Knowledge Digital Library [Internet]. Available from: http://www.tkdl.res.in/tkdl/langdefault/common/Home.asp?GL=Eng.
  20. UMC | Uppsala Monitoring Centre [Internet]. Available from: https://www.who-umc.org/.
  21. Pharmacovigilance Programme of India [Internet]. Available from: https://ipc.gov.in//PvPI/about.html.
  22. Pharmacovigilance - All India Institute of Ayurveda, New Delhi [Internet]. Available from: https://aiia.gov.in/pharmacovigilance/.
  23. Ranjan A, Tripathi A, Saurabh A, et al. Signal detection in pharmacovigilance: an application of subjective Bayesian inference. Adv Pharmacoepidemiol Drug Saf 2016;5:207.
  24. Madigan D, Ryan P, Simpson S, et al. Bayesian methods in pharmacovigilance. Bayesian Statistics 2010;9:1–17.
  25. Suling M, Pigeot I. Signal detection and monitoring based on longitudinal healthcare data, pharmaceutics. 2012;4(4):607–640.
  26. Bhavaprakasha Prathamkhanda, Mishrakavarga, Guducyadivarga/228, accessed from e-nighantu, niimh.nic.in.
  27. Kshirsagar NA, Dalvi SS, Joshi MV, et al. Phenytoin and ayurvedic preparations: clinically important interaction in epileptic patients. J Assoc Physicians India 1992;40(5):354–355.
  28. Dandekar UP, Chandra RS, Dalvi SS, et al. Analysis of a clinically important interaction between phenytoin and Shankhapushpi, an ayurvedic preparation. J Ethnopharmacol 1992;35(3):285–288. DOI: 10.1016/0378-8741(92)90026-N.
  29. Johnson K, Guo C, Gosink M, et al. Multinomial modelling and an evaluation of common data mining algorithms for identifying signals of disproportionate Reporting in pharmacovigilance database. Bioinformatics 2012;28(23):3123–3130. DOI: 10.1093/bioinformatics/bts576.
  30. Agresti A. Categorical Data Analysis. John Wiley & Sons; 2002. pp. 34–35.
  31. Agresti A. Categorical Data Analysis. John Wiley & Sons; 2002. pp. 28–31.
  32. Armitage P, Berry G, Matthews JNS. Stat Methods Med Res,. Blackwell Science Publishing; 2002. pp. 671–675.
  33. Agresti A. Categorical Data Analysis. John Wiley & Sons; 2002. pp. 65–90.
  34. Armitage P, Berry G, Matthews JNS. Stat Methods Med Res,. Blackwell Science Publishing; 2002. pp. 187–204.
  35. http://www.ccras.nic.in/sites/default/files/Guidelines_for_prevention_and_management_of_Diabetes.pdf.
  36. Armitage P, Berry G, Matthews JNS. Stat Methods Med Res. Blackwell Science Publishing; 2002. pp. 165–183.
PDF Share