Why AI Solutions are Necessary for Quality Control in Pharmaceutical Manufacturing

The Tokyo Olympics encountered more than its share of challenges and controversies. The pressure and scrutiny on athletes take a toll in a competition where the smallest mistakes are the difference between winning gold or not winning. Missteps and factors out of the realm of their control can prevent any athlete from competing altogether.

Brady Ellison, a U.S. archer with three Olympic Gold Medals, was nearly disqualified for the Tokyo games because he tested positive for a diuretic that has been banned by the World Anti-Doping Agency (WADA). Further investigation uncovered that Ellison did not take the diuretic.

Instead, the culprit responsible for the positive result was the medication company that manufactures Ellison’s thyroid medicine. The company used the same tablet press to manufacture the thyroid medicine and the banned diuretic. Because of poor quality control and improper decontamination, the thyroid medicine was tainted with traces of the diuretic.

The Olympic drug test detected the diuretic irregularity and nearly cost Ellison his dreams of competing. Fortunately, Ellison challenged the initial results and was able to switch to an untainted thyroid medication. His story isn’t an isolated incident. Contaminated medications and supplements contributed to 32 Olympic disqualifications since 2016. The consequences of tainted medication and supplements can be more dangerous, and sometimes deadly, to unknowing consumers without access to testing.

Cross-contamination is a known problem in the pharmaceutical industry

Contaminated tablet presses resulting from product switchover and lack of proper quality control is an urgent challenge for drug manufacturers. In recent years, supply chains, product development, manufacturing, and quality control have increased in complexity with more active ingredients in drugs and an escalated demand that is rapidly growing.

To keep up with demand and reduce costs, the production footprint for pharma companies is dependent on outsourced and offshore manufacturing. With COVID-19 accelerating, the surge of outsourcing in the contract manufacturing market has grown from $900B in 2017 to $1.17T in 2021.

While there are cost-savings, efficiencies, and tradeoffs in outsourcing manufacturing, it poses a new challenge around quality control to minimize contamination as much as possible, while enforcing additional safety and risk measures on behalf of a third-party.

These increasing complexities affect an already exhausted quality control team dealing with faster turn times, increased product switchovers, and sometimes antiquated quality processes. The increase in clients at many contract manufacturers may also reduce the focus paid to quality control around cross-contamination.

Increased complexity in drug manufacturing is a by-product of the COVID-19 pandemic. It has created a climate of added pressure from public, government, and regulatory bodies with constantly changing new health guidelines and mandates. All of this pressure, change, churn adds to the burden the pharma companies and contract manufacturing teams endure.

Lack of quality control can severely impact a drug manufacturer

The lack of oversight and proper quality control imposes severe repercussions for any drug manufacturer and their customers. The chance of contamination puts customers at risk for potentially life-threatening allergic reactions. The impact of those errors can result in litigation, reputational harm, and FDA regulatory action.

Since 2018, more than 350 drugs have been recalled. Recalls are the result of contaminated tablet presses or other quality control issues. The FDA is aware of these cross-contamination issues and is devoting more attention to it. As the Bloomberg article cited notes, “the FDA publicly reprimanded 21 companies for cross-contamination [in 2019]. In many other instances, federal regulators handled it behind closed doors.”

During the COVID-19 pandemic, the pharma industry has been under increased scrutiny. Continuous testing, trials, the Olympics, and drug prices have a significant impact on drug manufacturers that can cost valuable time, reputations, and profits.

Pharma must adopt AI and automation technologies to mitigate this risk

Drug manufacturers must choose the capabilities AI and new technologies provide to mitigate quality control, production, and manufacturing challenges faced by these increasing complexities and pressures.

Many pharma leaders have identified AI as a transformative technology in drug testing and R&D, using it to process and analyze biomedical and clinical data, design new drug molecules, and improve the clinical trial process.

By 2024, the AI market within the biopharma industry is expected to reach $10B. Early adopters to AI will realize those benefits quickly:

  • AI makes the entire production process more efficient by automating tasks, speeding up processes with precision, and reducing waste. 
  • More effective AI tools can identify and streamline areas within production and quality more effectively, improving accuracy.
  • AI greatly improves risk detection and identifies cross-contamination faster and more accurately than humans so they can focus on addressing maintenance issues, keeping downtime to a minimum. 
  • Many AI tools offer predictive maintenance capabilities, which pre-empt the need for maintenance, boosting overall efficiency.

With the novel insights powered by machine learning and big data analysis, a new class of high-performing drug manufacturers is emerging, in large part because of their ability to quickly adopt new AI technologies tailor-made for their industry. This allows them to be proactive for priorities like R&D, drug design, clinical trials, production, manufacturing challenges, and quality control. 

For example, leading manufacturers can employ anomaly detection algorithms, like normal behavior modeling (NBM), to reduce risk in their production lines through proactive identification of ingredient and raw material anomalies, formula mix irregularities, and overall process and product quality issues that would otherwise have an outsized negative impact if left undetected.

These departmental and organization improvements provide an outcome with better products, significantly reduced risk of contamination, and more cost-effective manufacturing process. It also reduces the likelihood of drawing negative public attention and scrutiny from other government regulators and the FDA.

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