FDAによるデータインテグリティに関する指摘が減少
RAPSの記事によると、FDAの査察がリモートになったことで、データインテグリティに関する指摘が減少しているという。
当該記事は、FDAのconsumer safety officerであるMaan Abduldayem氏が第46回国際GMP会議で講演した内容についてまとめたものだ。
データインテグリティに関する指摘の推移
- FY2021：5つのWarning Letter（W/L）で指摘
- FY2020：24W/Lで指摘
- FY2019：32W/Lで指摘
やはりリモート査察には限界があるようだ。
コロナ禍が収束すれば、FDAはオンサイト査察を再開するだろう。
データインテグリティの確認ができない場合、患者の安全性、製品の品質の保証ができないことになる。
例えば、QCラボにおける品質試験において、故意か過失かに関わらずデータの改ざんが発生している場合、患者の安全性にかかわることとなる。
つまり、本来は出荷すべきでない品質不良の製品を出荷してしまうリスクがあるためである。ここで重要なことは、故意による改ざんはもってのほかであるが、過失（ヒューマンエラー）によるデータの信頼性欠如も問題になるということである。
故意であれ、過失であれ、データの信頼性が失われた場合、患者の安全性には双方ともに影響が出るためである。一般に過失に基づくデータインテグリティの欠如は、全体の80%を占める。具体的には、転記ミス、計算ミス、操作ミスなどである。
また、故意といっても不正とは限らず、本人は是と信じて生データなどを変更する場合がある。
これらは、勘違い、思い違い、記憶ミス、SOPの不十分な理解、あいまいなSOPなどが原因となる。
一方において、製品の品質は、製造記録やバリデーション記録の不備等によって脅かされることになる。
このようにデータインテグリティの確認ができない場合、FDAは患者の安全性、製品の品質を確認できず、出荷を認めることができないことになるのである。
また、PAI（Pre-Market Inspection：承認前査察）の場合は、データインテグリティの確認ができないと、FDAが申請資料を受け入れて良いかが判定できず、期限内の承認/不承認の判定に影響することとなる。
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