This presentation explores the critical role of database applications in research and industry, with a focused perspective on Ayurveda, pharmaceutical sciences, and healthcare systems. It highlights how modern databases act not merely as data storage systems, but as active engines for scientific discovery, quality assurance, regulatory compliance, and evidence-based decision-making. The presentation covers real-world applications of databases in phytochemical screening, drug discovery, omics data integration, clinical trial registries, GMP compliance, supply chain traceability, and intellectual property protection. Special emphasis is placed on the role of databases in clinical decision-making and public health policy development, demonstrating how structured data and real-world evidence influence preventive healthcare strategies and integrative medicine frameworks. The presentation also discusses future directions in database development, including AI/ML-driven predictive analytics, federated learning, semantic web technologies, cloud-native architectures, distributed databases, and FAIR data principles, highlighting their relevance to next-generation healthcare and pharmaceutical research. This resource is intended for students, researchers, academicians, healthcare professionals, and policy analysts interested in the intersection of data science, pharmaceutical research, and traditional medicine modernization.

1. DATABASE
APPLICATIONS
IN RESEARCH &
INDUSTRY
PRESENTED BY:
DR. AMAN BHARDWAJ
P.G. DEPARTMENT OF RAS SHASHTRA AND BHAISHJYA KALPANA
[M.M.M. GOVERNMENT AYURVEDA COLLEGE, UDAIPUR]

2. WHAT IS A DATABASE?
(SCIENTIFIC CONTEXT)
Definition:A structured, scalable
system to store, retrieve, and
analyze data.
The Hierarchy of Data (The
DIKW Pyramid):
Data: Raw numbers (e.g., pH value,
absorbance).
Information: Context (e.g., Quality Control
pass/fail).
Knowledge: Pattern recognition (e.g., Herb
X works for DiseaseY).
Wisdom: Policy decisions (e.g., Banning
specific toxic constituents).

3. WHY DATABASES MATTERTODAY
• The Problem:
• Explosion of "Big Data" in Pharma (Genomics, Clinical, Supply Chain).
• Manual handling is impossible;Traditional Knowledge (TK) is currently
fragmented.
• The Solution:
• Structured Repositories: Digitizing ancient texts (Samhitas).
• Analytical Engines: Mining connections between Dravya (substance)
and Guna (property).
“In the 21st century, the bottleneck of discovery isn't the lack of
resources, but the inability to organize them.”

4. DATABASE APPLICATIONS IN
RESEARCH

5. RESEARCH APPLICATIONS:
OVERVIEW
Phytochemical
Screening:Virtual
identification of active
principles.
Network
Pharmacology:
Mapping multi-target
drug actions
(Holistic approach).
Omics
Integration:
Linking genes to
Prakriti.
Clinical Evidence:
Registries and meta-
analysis.

6. PHYTOCHEMICAL SCREENING
&
DRUG DISCOVERY
The Challenge:
Ayurveda uses complex
mixtures; modern
pharma looks for single
molecules. Databases
bridge this.
Key Databases (Essential
Additions):
IMPPAT: (Indian Medicinal
Plants, Phytochemistry And
Therapeutics) – Largest curated
database linking plants to
phytoconstituents.
GRAYU: Knowledge graph for
Ayurvedic formulations.
PubChem / ChEMBL: Global
chemical databases for cross-
referencing structure activity.
Impact:
Reduces wet-lab cost by
60% via "Virtual
Screening."

8. AYURGENOMICS & ‘OMICS’
INTEGRATION
• Correlating Tridosha with Genomic markers (SNPs).
Concept:
• Genomic Data: DNA sequences (NCBI GenBank).
• Phenotypic Data: Prakriti assessment data.
Data Integration:
• Understanding the molecular basis of Rasayana therapy.
• Moving from "One size fits all" to "Precision Ayurveda."
Application:

9. CLINICAL REGISTRIES
&
EVIDENCE SYNTHESIS
Why Registry? Combatting publication bias
(reporting only positive results).
Major Registries:
CTRI (CLINICALTRIALS REGISTRY -
INDIA): MANDATORY FOR
REGULATORY APPROVAL.
AYUSH RESEARCH PORTAL:
SPECIFICALLY FORTRADITIONAL
MEDICINE EVIDENCE.
Real-World Evidence
(RWE):
MINING ELECTRONIC HEALTH
RECORDS (EHR)TO SEE HOW
AYURVEDIC DRUGS PERFORM IN
ACTUAL HOSPITAL SETTINGS, NOT
JUST CONTROLLEDTRIALS.

10. LITERATURE MINING:THE
DIGITAL SAMHITA
TheTask: Extracting hidden relationships from
millions of papers.
Tools:
DHARA: DIGITAL HELPLINE
FOR AYURVEDA RESEARCH
ARTICLES.
PUBMED / SCOPUS: GLOBAL
BIOMEDICAL INDEXING.
Benefit:
VALIDATES "REVERSE
PHARMACOLOGY" (BEDSIDE
TO BENCH).
PREVENTS DUPLICATION OF
RESEARCH EFFORTS.

11. DATABASE APPLICATIONS
IN INDUSTRY

12. INDUSTRY APPLICATIONS:
OVERVIEW
Quality: Ensuring standard markers in herbal raw materials.
Safety: Pharmacovigilance (Adverse Drug Event reporting).
Commerce: Supply chain and Inventory management.
Legal: Intellectual Property Rights (IPR).

13. GMP COMPLIANCE & QUALITY
MANAGEMENT
LIMS (Laboratory
Information
Management
Systems):
Automated tracking of samples
from "Quarantine" to
"Approved."
Audit trails prevent data
manipulation (Data Integrity).
Standardization
Databases:
Storing HPTLC/HPLC
fingerprints of standard
reference markers.
Comparing batches against a
"Gold Standard" digital profile.

14. SUPPLY CHAIN & BLOCKCHAIN
TRACEABILITY
The Issue: TheTech Solution:
Blockchain Database: An
immutable ledger.
Traceability: Scanning a QR
code to see the farm location,
harvest date, and lab test results
of the specific batch.
Adulteration and
substitution in raw
herbs.

16. IP PROTECTION:THE CRITICAL
SHIELD
TheThreat: Biopiracy
(Patenting existing
traditional knowledge).
The Defense:TKDL
(Traditional Knowledge
Digital Library):
A unique database by CSIR & AYUSH.
Translates Sanskrit texts into 5
international languages.
Impact:
Has successfully overturned 200+
wrong patent claims globally.

17. DATABASES FOR DECISION-
MAKING & POLICY

18. CLINICAL DECISION SUPPORT
SYSTEMS (CDSS)
Concept: "AI-Assistant" forVaidyas.
Function:
Input: Patient symptoms +
Prakriti + Season (Ritu).
Database Output: Suggested
Shodhana (purification) or
Shamana (palliative) therapy
based on 10,000+ similar past
cases.
Goal: Minimizing diagnostic errors.

20. PUBLIC HEALTH & POLICY: GLOBAL
RECOGNITION
Big Data for
Policy:
NAMER: National Ayurveda Morbidity
and Epidemiological Records.
ICD-11 Module 2 (Crucial Addition):
The WHO has integratedTraditional
Medicine into the International
Classification of Diseases (ICD).
Significance:
Allows Ayurveda morbidity stats to
be compared globally.
Policies can now be funded based on
concrete data, not just anecdote.

21. FUTURE DIRECTIONS &
EMERGINGTECHNOLOGIES

22. AI &
PREDICTIVE
TOXICOLOGY
(IN SILICO)
Shift: Testing on computers (In
Silico) before animals (InVivo).
ADMET Databases: Predicting
Absorption, Distribution,
Metabolism, Excretion, and
Toxicity.
Benefit: Ethical reduction in
animal testing and massive cost
savings.

23. FEDERATED
LEARNING
(COLLABORATIVE
INTELLIGENCE)
The Problem: Hospitals
cannot share patient data due
to privacy (HIPAA/DISHA).
The Solution: Federated
Learning.
• The AI model travels to the hospital,
learns from the data, and comes
back "smarter."
• The patient data never leaves the
hospital server.

25. CONCLUSION
Databases are the "Nervous System"
of modern pharmaceutical science.

26. THANK
YOU