Pharmacovigilance is a concept defined by the World Health Organization (WHO), specifically "science and activities related to the discovery, assessment, understanding and prevention of adverse reactions or any other drug-related problems", which plays a key role in ensuring the safety of patients' medication. Creative Biolabs helps customers control and understand adverse drug reactions through a variety of methods, including spontaneous reporting, enhanced monitoring and database research. At the regulatory level, these measures include conditional approvals and risk management plans; from a scientific perspective, transparency and increased patient participation are two important factors.
Adverse Drug Reaction (ADR) Monitoring
In recent years, the issue of drug safety has received widespread attention. However, the large number of reported related articles can also cause patients and medical professionals to worry unnecessarily about the use of some drugs. A more serious consequence may be that the patient stops taking prescription drugs, which may lead to a more serious situation. To prevent this, a reasonable drug safety test is required. To help pharmaceutical companies deliver drugs of positive interest to the public, Creative Biolabs provides customers with pharmacovigilance services from the following aspects:
Fig.1 A framework for ADR detection and extraction from social media data. (Sarker, 2015)
Clinical trial data monitoring. The main method of collecting drug information in the pre-marketing phase is through clinical trials. Double-blind randomized controlled trials are generally considered to be the most rigorous method to determine whether there is a causal relationship between treatment and outcome. However, due to the limited number of participating patients, it is often not possible to identify rare ADRs. Continuous follow-up monitoring of the drug needs to be continued after marketing.
Data mining in spontaneous reporting. The term "data mining" refers to the principle of analyzing data and extracting relevant information from different perspectives. Algorithms are often used to determine associated hidden patterns or unexpected situations.
Intensive monitoring. With its non-intrusive nature, intensive surveillance provides real-world clinical data throughout the collection period without involving inclusion or exclusion criteria. It is unaffected by selection and exclusion criteria that characterize clinical trials, thereby eliminating selection bias. Another advantage of this method is that it is based on event monitoring, so it can identify event signals that are not necessarily suspected to be ADRs of the drug under study. A rigorous monitoring program also enables adverse events to be estimated, and therefore the risk of certain ADRs.
Drug risk management. The introduction of a risk management plan (RMP) is a more proactive approach to drug aftermarket monitoring. Such RMPs are being set up in order to identify, characterize, prevent or minimize risk relating to medicinal products, including the assessment of the effectiveness of those interventions.
Once a pharmacovigilance signal is generated in a clinical trial, it must be validated. Therefore, while data mining can be used to detect potential signals and may imply hypotheses related to those signals, it alone cannot prove a direct causal relationship between drugs and ADE. Other sources of safety data (i.e., clinical trial data, medical literature, etc.) must be analyzed to confirm the clinical significance of the pharmacovigilance signals generated by data mining. If the signal is verified and there is a causal relationship between the drug and the ADE, the FDA may issue a recall, change the drug label, or take the drug out of the market. Creative Biolabs provides clinical pharmacovigilance assessment services that can help you reduce your business risks and unnecessary losses.
If you are interested in our pharmacovigilance services, you can contact us for more details.
Sarker, A.; et al. Utilizing social media data for pharmacovigilance: a review. Journal of biomedical informatics. 2015, 54: 202-212.