Challenges in Application of Bioanalytical Method on Different Populations and Effect of Population on Pk
Bibliographic record
Abstract
Prashant Kale has 22 years of immense experience in the analytical and bioanalytical domain. He is Senior Vice President, Bioequivalence Operations of Lambda Therapeutic Research, India which includes Bioanalytical, Clinics, Clinical data management, Pharmacokinetics and Biostatistics, Protocol writing, Clinical lab and Quality Assurance departments. He has been with Lambda for over 14 years. By qualification he is a M.Sc. and an MBA. Mr. Kale is responsible for the management, technical and administrative functions of the BE unit located at Ahmedabad and Mumbai, India. He is also responsible for leading the process of integration between bioanalytical laboratories and services offered by Lambda at global locations (India and Canada). Mr. Kale has faced several regulatory audits and inspections from leading regulatory bodies including but not limited to DCGI, USFDA, ANVISA, Health Canada, UK MHRA, Turkey MoH, WHO. There are many challenges involved in the application of bioanalytical method on different populations. This includes difference in equipment, material and environment across laboratories, variations in the matrix characteristics in different populations, differences in techniques between analysts such as sample processing and handling and others. Additionally, there is variability in the PK of a drug in different populations. This article shows the effect of different populations on validated bioanalytical method and on the PK of a drug. Hence, the bioanalytical method developed and validated for a specific population may need required modification when applied to another population. Critical consideration of all such aspects is the key to successful implementation of a validated method on different populations.
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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 | 0.093 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".