The race to find therapeutics and a vaccine in the fight against COVID-19 has put the spotlight back on international clinical trials.
Although international clinical trials are necessary, they often occur in unfamiliar business environments among ever-changing governmental regulations, which means pharmaceutical manufacturers must work with third-party clinical review organizations (CROs) that have local expertise. CROs, in turn, often interact with healthcare professionals (HCPs) who support the CROs as clinical trial consultants and/or investigators, and may be considered “foreign officials” under the U.S. Foreign Corrupt Practices Act (FCPA) or other anti-corruption laws. These interactions carry risk for pharmaceutical manufacturers, and may be misinterpreted as inappropriate interference with the HCP’s independence and/or used to support an investigation or enforcement action against the company.
CRO activities also frequently involve the handling of company funds, as well as payments to and interactions with government agencies, all of which require careful compliance oversight. Further, companies often need to rely on CROs in countries with emerging markets and developing enforcement and regulatory regimes – factors that independently raise the risk of potential bribery and corruption issues.
These concerns are not academic; even companies with subsidiaries local to the country in which clinical trials occur have been subject to large-scale enforcement actions based on the subsidiary’s alleged interactions with HCPs. In 2018, for example, a Securities and Exchange Commission (SEC) enforcement action based in large part on a local subsidiary’s allegedly improper interactions with HCPs – including clinical trial fees that were allegedly made without proper documentation of the work performed or the identity of the recipient – resulted in a cease-and-desist order for more than US$20 million in fines, disgorgement, and prejudgment interest. In 2020, the SEC and the U.S. Department of Justice (DOJ) resolved a wide-ranging investigation at a cost to that company of more than US$330 million, including US$225 million in criminal fines. Among the issues resolved were allegations of misconduct in connection with the local subsidiary’s payments to HCPs for Phase IV clinical studies.
Adding intermediaries such as CROs to the mix potentially compounds the bribery and corruption risks at play. At the same time, traditional compliance tools – such as due diligence, screening, and spot monitoring and auditing – are labor-intensive activities that can strain compliance budgets. Fortunately, emerging technology can help compliance officials conduct proactive and effective third-party CRO oversight by supplementing traditional compliance roles with data analytics.
Companies are increasingly turning to new and innovative risk-management technologies, which can spot and assess patterns of problematic conduct quickly and efficiently. Machine learning and artificial intelligence (AI) in particular can potentially help mitigate the risks inherent in international clinical trials.
Machine learning algorithms use statistics to find patterns in large amounts of data. Such algorithms are a form of AI that, when used correctly, can
- spot patterns indicative of risky and/or non-compliant conduct in real time;
- help identify, prioritize, and mitigate risk areas;
- streamline compliance monitoring efforts in regulation-heavy areas such as General Data Protection Regulation (GDPR); and
- eliminate “false positive” responses that sometimes occur with more standard data monitoring.
As with every tool, there are challenges to these promising new technologies. Although machine learning and AI can help keep costs down over time, they require substantial investment early on – both in terms of monetary resources and human input – as machine learning applications are only as robust as the data inputs and algorithms developed to allow them to function independently.
Machine learning and AI require direct and ready access to a broad data set – preferably one that is updated in or as close to real time as practicable. In addition, they require front-end work to create properly calibrated software and algorithms that allow the tools to interpret the data meaningfully and reliably.
Once established, however, machine learning and AI reduce both labor costs and compliance risks by continuously sifting through data to identify common red flags – particularly in high-risk, highly regulated areas such as international clinical trials and CRO management.
Using new systems effectively
To date, companies have successfully used machine learning and AI to
- assess whether their CRO has “good” (i.e., compliant) relationships with HCPs, government officials, and local agencies by analyzing the nature and frequency of those contacts;
- monitor the timing of CRO-led regulatory submissions, recruitment of researchers, and other third-party interactions for potential AB&C issues by identifying anomalies in the expected timing of milestones;
- fill in gaps and identify potential issues concerning CRO contracts and payments to subcontractors or other third parties – particularly when transparent contractual setups or payment structures are absent; and
- spot financial connections between CROs, subcontractors and government officials by analyzing and identifying personnel, financial, and organizational overlap between CROs and investigators or other HCPs, government officials, and local agencies.
Embracing emerging technology does not remove the need for robust, well-staffed compliance programs. It can, however, allow compliance groups to make data-driven decisions about what to investigate and where to focus their training, auditing, and remediation efforts – even in the face of scarce human resources. Particularly in the context of international clinical trials, where AB&C risks are inherently higher and in-person monitoring capabilities are reduced, machine learning and AI provide promising compliance protection.
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