Addressing risks is a significant undertaking which includes high level business commitment, budgeting, project management, cross-functional collaborations, and disciplined workflows. This checklist offers real-world insights on a comprehensive risk mitigation strategy to help you develop robust scientific and therapeutically productive studies.
Pharmaceutical and biotech companies aim to bring their therapeutics to market as soon as possible. A streamlined, efficient, and compliant process from discovery through development helps avoid a wide range of potential problems, including delayed identification of drug safety liabilities and efficacy issues, as well as the accompanying major cost overruns.
In reality, preclinical drug discovery frequently encounters multiple risks, jeopardizing the significant investments made by pharma organizations. A literature review published in the Journal of Translational Medicine found that most companies had not produced enough drugs in the preceding years to reach their goals for product innovation leading to new molecular entities. While the specific causes for underperformance vary from one organization to another, there are risks that are common across the industry.
From managing large quantities of data on drug candidates to struggling with the reproducibility of studies, preclinical teams must navigate multiple challenges and risks en-route productive drug discovery, some of which are reviewed here in greater detail.
1. Big data management
The discovery stages generate an enormous amount of data. Scientists start with a large library of compounds and/or biologics, with the goal of discerning the therapeutic and modulation effects via mechanism of action (MOA). Functional assays for safety and bioavailability follow including animal studies.
Data from these studies leads to decision-making processes that predict a drug candidate’s viability. For example, will it be an efficacious and safe drug targeted to treat the disease? The traditional workflows for these tasks are often slow, inefficient, and at times manual. Managing the constant data output further impedes the drug discovery process. It is a huge task mitigating these risks and challenges in light of protracted timelines in drug development.
Data analysis and management is witnessing migration to electronic systems for both metadata as well as structured data, and the advent of artificial intelligence (AI) will enable seamless analytics capabilities. AI, in particular has shown potential, as in startup BERG’s work on the pancreatic drug BPM31510.
2. Data integrity and cybersecurity
The pharma industry has gradually come under more scrutiny from regulatory bodies such as the FDA for compliance with data integrity requirements. Equipment qualification and computer system validation are both key steps toward data integrity, especially within modern laboratories using instruments from multiple technology providers.
Ideally, lab teams would have defined processes for the full data lifecycle, from collection to archiving to final data destruction. This would ensure that the data is complete, consistent, and accurate, per the principles of ALCOA-Plus, allowing for better comparison between datasets thus improving data reproducibility.
Additionally, there are many testing and analyses projects that are outsourced to multiple contract research organizations (CROs). Silos can arise when trying to integrate their reports with other data sources and balance collaborative workflows with platform security. Therefore, it is imperative these silos are addressed when ensuring data integrity and cybersecurity.
On the cybersecurity front, the growing digitization of the lab raises the possibility of attacks that can exfiltrate sensitive data or bring operations to a halt. The 2017 case of the WannaCry ransomware exhibits some of the possible dangers. That threat rendered infected IT systems unusable until a ransom was paid to the attackers. At least one global pharma firm was targeted by WannaCry in particular. More broadly, ransomware and other forms of malware are significant risks to any modern lab and associated research process.
3. IT and personnel management
Data integrity within drug discovery generally requires well-coordinated teamwork. Skilled IT staff are a pivotal piece in this puzzle, which is why it’s important to make sure that IT strategy is aligned with that of the groups that they support in the organization.
For example, IT may be focused on implementing more standardized and secure forms of data analysis, but that goal may not be aligned with the business objectives of greater operational agility. Another potential challenge could be limited IT resources to support an entire organization.
Skills shortages in technical disciplines and limited budgets across many IT departments may result in bottlenecks. With proper training, workflow demands could potentially be met by generalists, alleviating the challenges and continue drug discovery on a steady course.
4. Technical problems
The costs of bringing just one new prescription drug to market rose 145% from 2003 to 2013, to over $2 billion. Failing before receiving marketing approval is costly and can be triggered by a variety of technical issues related to testing and evaluating a candidate.
Within discovery and development, various factors can impact drug candidate evaluation and validation. For example, from the unavailability of appropriate assays and associated consumables to IT and informatics challenges identified above can hamper discovery and creation of new intellectual property (IP).
Project management (PM) and risk mitigation plans should be in place at every stage of preclinical discovery. The PM teams should work closely with lab personnel and other stakeholders to flag and contain relevant technical risks.
5. Translational issues
Risks in drug development can also be minimized by establishing a stronger target to disease association via target validation and subsequent identification of drug candidates specific to target interaction and disease.
Trends in translational research from bench to bedside can enable stratification of patients that can likely respond to a drug. An alternate approach devoid of translational guidance can become more time-consuming and costly, and could potentially have adverse effects on non-responding populations.
Hence, translational strategies, as part of risk management and precision medicine, may not only identify responding patient cohorts and minimize non-responding populations but also enable pharma companies upfront to evaluate the addressable market and return on investment.
6. Reproducibility risks
Pivotal drug research often originates in academia or within startups. However, at times repeatability of studies and associated data reproducibility may pose challenges.
There are countless instances of initially promising drug discovery research that eventually led to failure, such as the work between 2006 and 2009 on a melanoma treatment based on using antibodies to determine if a patient needed treatment.5
Preventing such episodes is not always possible. However, validated standard operating procedures (SOPs) or methodologies, reproducible experimental setups, highly skilled workforce, project management teams, and risk mitigation plans can minimize major surprises that could undermine the drug discovery and development process.
Comprehensive risk mitigation strategy for preclinical drug discovery
Addressing the risks highlighted above is a significant undertaking which includes high level business commitment, budgeting, project management, cross-functional collaborations, and disciplined workflows resulting in robust scientific and therapeutically productive studies.
In addition, drug developers can harness the power of outsourced or co-sourced lab solutions to establish above measures via turn-key solutions for discovery, development, information management, regulatory compliance, and other multiple needs.