Sourcing of Quality Raw Materials for Indian System of Medicine (ISM) and Botanical Drugs
Bibliographic record
Abstract
Globally, there has been an unparalleled growth in the plant-derived medicinally useful formulations, drugs and health care products, with annual growth rates between 10–20% in most of the countries. According to WHO, the international market of herbal products is estimated to be US$ 62 billion which is poised to grow to US$ 5 trillion by the year 2050. This has attracted many large pharmaceutical and consumer product companies worldwide to have herbals/botanicals in their product portfolio. India is no exception to it and has a competitive edge as Indian Traditional drugs/products, have their roots in time tested systems of medicine namely, Ayurveda, Unani and Siddha. Renewed interest in botanical products has resulted into a huge international trade in raw plant material, feeding a range of such industries, including the $20 billion botanical medicine market. Presently between 75 and 85% of the raw materials for the botanical industry are sourced from wild. Due to the increasing public demand for quality botanical products, some companies are now making efforts to acquire at least a portion of their raw material from sustainable and ethical sources, but most invest little in this side of their business. The existing industry practice often promotes poor management of species and few benefits for the collectors and cultivators, and many companies remain distant and unaware of the conditions under which raw materials are sourced. However, there also exist opportunities to create change in this sector. The source and quality of raw materials, storage, post-harvest handling play a pivotal role in guaranteeing the quality and stability of ISM & botanical preparations. In India, in addition to the promotion of cultivation of medicinal and aromatic plants (MAPs) by certain government departments and R&D institutes, of late some private herbal drug industries have also started sourcing their requirement of herbal raw material from cultivated sources. The cultivation of MAPs, on the other hand, would not only lead to better control over quality of the end products but will also reduce anthropogenic stress on wild stands. The presentation will illustrate the efforts being made in India in general and at the Indian Institute of Integrative Medicine (IIIM – CSIR) in particular for the sourcing and sustainable supply of raw materials for ISM & Botanical industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".