INVALIDATED OOS RATE (IOOSR) IN PHARMA
What is OOS?
OOS means Out of Specification. When test result goes outside the approved specification limit, it is called OOS.
Example: Assay specification is 95.0% to 105.0%. If result comes 93.2%, it is OOS.
OOS can happen in Raw Material, In-process, Finished Product, Stability testing.
There are two more terms you should know:
- OOS – Out of Specification: Result outside spec (e.g. Assay 93% when spec is 95-105%)
- OOT – Out of Trend: Result within spec but outside trend (e.g. Assay 98% but last 10 batches were 100-101%, so 98% is OOT)
- OOE – Out of Expectation: Unexpected result, not as per previous experience
What is Invalidated OOS Rate (IOOSR)?
IOOSR is one of the 4 main Quality Metrics proposed by FDA in Quality Metrics Guidance 2015. This is the most critical metric for FDA because it directly shows Data Integrity and Quality Culture.
Formula of IOOSR:
Example:
In one year, total 50 OOS occurred in lab. After investigation, 40 OOS were invalidated as lab error and batch was released based on retest. 10 OOS were confirmed as manufacturing failure and batches rejected.
IOOSR = (40 / 50) x 100 = 80%
80% IOOSR means 80% of OOS you showed as lab error.
Why FDA is Very Strict About IOOSR?
If IOOSR is high (e.g. > 70-80%), it means company is hiding manufacturing failures as lab errors.
Real manufacturing problem: Tablet assay low due to poor mixing.
What company shows: Analyst made dilution error, so OOS invalidated, retest passed, batch released.
This is manipulation. So FDA watches IOOSR very closely. High IOOSR is red flag for warning letter.
Low IOOSR (e.g. 10-20%) means company honestly accepts manufacturing failures and does proper investigation. This shows good quality culture.
Two Types of OOS – Lab Error vs Manufacturing Error
| Lab Error (Assignable Cause in Lab) | Manufacturing Error (True OOS) |
|---|---|
| Analyst error – Dilution mistake, Calculation mistake | Manufacturing problem – Poor mixing, Low hardness, Degradation |
| Instrument error – HPLC column problem, Balance not calibrated | Formulation problem – API degradation, Interaction |
| Sample handling error – Sample contaminated, Wrong sample taken | Raw material problem – API assay low |
| Glassware / Reagent error – Expired reagent, Wrong mobile phase | Process problem – Drying incomplete, Blending issue |
| OOS can be invalidated and retest allowed | OOS cannot be invalidated. Batch must be rejected or investigated further |
Note: To invalidate OOS as lab error, you need clear, documented evidence. You cannot just say “analyst error” without proof.
How to Investigate OOS – As per FDA OOS Guidance 2006
FDA says OOS investigation has two phases:
Phase 1 – Laboratory Investigation (Phase 1A and 1B)
– Check analyst: Is analyst trained? Did analyst follow SOP?
– Check instrument: Is HPLC calibrated? Column okay? Balance calibrated?
– Check solution: Any dilution mistake? Calculation mistake? Reagent expired?
– Check sample: Sample integrity okay? Any spillage?
– This is objective, quick check. No retesting yet.
If clear lab error found in Phase 1A (e.g. analyst spilled sample, calculation mistake), OOS can be invalidated with justification and retest done.
– Retest the original sample preparation (if sample left)
– Re-prepare and retest by same analyst
– Check other samples tested on same instrument that day – Did they also fail?
– Review training records, instrument logs
– If lab error still not found, go to Phase 2
Phase 2 – Full Scale Manufacturing Investigation
– Review Batch Manufacturing Record (BMR) – Any deviation during manufacturing?
– Review other batches – Did other batches also have low assay?
– Check raw material – API assay? Moisture?
– Check process – Blending, Granulation, Drying, Compression parameters
– Check stability – Any trend of assay decreasing?
– If manufacturing root cause found, batch must be rejected. Cannot invalidate OOS.
– If no root cause found even after Phase 2, FDA says you cannot invalidate. You must consider OOS as true failure and reject batch or do extended investigation.
Companies do retesting in Phase 1 without any investigation and if retest passes, they invalidate original OOS as lab error without any evidence. This is called Testing into Compliance and FDA strictly prohibits this.
As per FDA: “You cannot average OOS and passing results and release batch. OOS result remains OOS unless clear lab error proven.”
What is Good IOOSR? – Industry Benchmark
– Good IOOSR: 10% – 30% – Means most OOS are true manufacturing failures, company accepts them honestly.
– Average: 30% – 50%
– Poor / Red Flag: > 70% – Means almost all OOS shown as lab error. FDA will suspect data integrity.
But target should be to reduce total number of OOS itself, not just IOOSR.
How to Reduce IOOSR and Total OOS?
A. To Reduce Total OOS (Prevent OOS):
- Improve manufacturing – Better process validation, robust process
- Improve raw material control – Test API properly
- Improve training to analysts – Most OOS are due to analyst error if training poor
- Preventive maintenance of instruments – HPLC, Balance
- Use correct specification – Tight but realistic spec based on process capability
B. To Reduce IOOSR (Improve Investigation Quality):
- Do honest investigation – Don’t hide manufacturing failure as lab error
- Define what is acceptable evidence for lab error – SOP should be clear
- Train analysts on OOS handling – Analyst should immediately inform supervisor, not retest secretly
- QA should review every OOS invalidation – QA must approve lab error with proper justification
- Track OOS trend – Monthly review of how many OOS due to lab vs manufacturing
- Do CAPA for repeated lab errors – If analyst does same mistake again, retrain, change SOP
IOOSR and Data Integrity
IOOSR is directly linked to Data Integrity ALCOA+ principles.
– Firm invalidated 25 out of 30 OOS as lab error without any documented evidence.
– Firm did 7 retests after OOS and reported only passing result.
– Analyst admitted that supervisor asked to invalidate OOS as analyst error to release batch.
– No Phase 2 manufacturing investigation done, all OOS closed as lab error in Phase 1.
All these companies got Warning Letters because high IOOSR indicated poor data integrity.
IOOSR Calculation in QMS – Practical Example
| Month | Total OOS | Invalidated as Lab Error | Confirmed as Manufacturing Failure | IOOSR |
|---|---|---|---|---|
| January | 8 | 2 | 6 | 25% |
| February | 12 | 10 | 2 | 83% – Red Flag |
| March | 5 | 1 | 4 | 20% – Good |
Trend: February IOOSR is 83% – Need investigation why so many lab errors in February. Check if one analyst, one instrument causing repeated OOS.
Conclusion: IOOSR is most sensitive Quality Metric. Low IOOSR does not mean good quality, but high IOOSR definitely means poor investigation or data integrity issue. Best way is to reduce total OOS by improving lab and manufacturing. And when OOS happens, do honest Phase 1 and Phase 2 investigation. Never test into compliance. Remember – OOS result is OOS unless clear lab error with documented evidence. FDA says – “When in doubt, OOS is manufacturing failure, not lab error.”
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About the Author
Mahummed Asif is a experienced pharmaceutical Quality Assurance professional and publisher of Pharmashare. He has worked with leading Pharmaceutical organizations and has developed extensive expertise in Quality Assurance, deviation management, investigations, CAPA, QMS, Product Life Cycle Management, change control, risk management, validation, product complaints, product recalls, and regulatory compliance. He is passionate about sharing practical pharmaceutical knowledge with professionals, students, and quality practitioners across the industry.