I wanted to check with you on aspects of case narratives in medical writing and how different it is from patient narratives as per ICH guidelines? Ramesh Shah Case or patient narratives refer to narratives of deaths, other serious adverse events and certain other significant adverse events. ICH E 3 Structure & Content of Clinical Study Reports requires the following details: 12.3.2 Narratives of deaths, Other serious adverse events and certain other significant adverse events. There should be brief narratives describing each death, each other serious adverse event, and those of the other significant adverse events that are judged to be of special interest because of clinical importance. These narratives can be placed either in the text of the report or in Section 14.3.3, depending on their number. Events that were clearly unrelated to the test drug/investigational product may be omitted or described very briefly. In general, the narrative should describe the following: The nature and intensity of event, the clinical course leading up to event, with an indication of timing relevant to test drug/investigational product administration; relevant laboratory measurements, whether the drug was stopped, and when; countermeasures; postmortem findings; investigator's opinion on causality, and sponsor's opinion on causality, if appropriate. In addition, the following information should be included: • Patient identifier. • Age and sex of patient; general clinical condition of patient, if appropriate. • Disease being treated (if the same for all patients this is not required) with duration (of current episode) of illness. • Relevant concomitant/previous illnesses with details of occurrence/duration. • Relevant concomitant/previous medication with details of dosage. • Test drug/investigational product administered, drug dose, if this varied among patients, and length of time administered.
What are the regulatory expectations for a drug product developed using artificial intelligence? Dr Suketu Chaudhury There are no regulatory guidelines from Indian health authorities. Recently, the US Food and Drug Administration has issued a draft guidance on considerations for the use of artificial intelligence (AI) to support regulatory decision-making for drug and biological products. This guidance discusses the use of artificial intelligence models in the drug product life cycle, where the specific use of the artificial intelligence model is to produce information or data to support regulatory decision-making regarding safety, effectiveness, or quality for drugs. This guidance recommends a risk-based credibility assessment framework that may be used for establishing and evaluating the credibility of an artificial intelligence model for a particular context of use. For the purposes of this guidance, credibility refers to trust, established through the collection of credibility evidence, in the performance of an artificial intelligence model for a particular context of use. The guidance describes:
IV. Considerations for artificial intelligence use in the drug product life cycle
A) A Risk-based credibility assessment framework
1. Step 1: Define the question of interest. 2. Step 2: Define the context of use for the AI model. 3. Step 3: Assess the AI model risk. 4. Step 4: Develop a plan to establish AI model credibility within the context of use.
a) Describe the model and the model development process. i. Describe the model. ii. Describe the data used to develop the model. iii. Describe model training.
b) Describe the model evaluation process. 5. Step 5: Execute the plan. 6. Step 6: Document the results of the credibility assessment plan and discuss deviations from the plan. 7. Step 7: Determine the adequacy of the AI model for the context of use.
B) Special consideration: Life cycle maintenance of the credibility of AI model outputs in certain contexts of use.
Besides this, there are several guidance documents from the US Food and Drug Administration and European Medicines Agency on diverse regulatory aspects of AI models.
Dr Arun Bhatt is a Consultant - Clinical Research & Development, Mumbai. Readers can send their queries at:arun_dbhatt@hotmail.com
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