Listicle: Maximising outsourcing models beyond time and cost

Challenges in outsourcing drug discovery

In this listicle, SAMDITech outlines the benefits of outsourcing models in the pharmaceutical industry, looking past perceived costs to the value outsourcing can bring.  

The goal of drug discovery is clear: Identify the most promising leads in the shortest amount of time. Many companies have several projects ongoing simultaneously, which can limit internal resources (space, lab equipment, personnel) and delay progress. Alternatively, some targets require novel, innovative solutions that may not be accessible in conventional drug discovery labs. For these reasons and others, many biotech and pharmaceutical companies – along with government research labs and academic institutions – rely on contract research organizations (CROs) for drug discovery services. CROs offer an established infrastructure to develop, initiate, execute, and progress drug discovery programs towards the clinic. Outsourcing can alleviate unnecessary backlogs and provide access to cutting-edge technologies, giving companies an edge to reach critical discovery milestones in a timely manner. However, while CROs have proven to be powerful resources for supporting drug discovery, working with CROs also presents several challenges (Figure 1). This white paper expands on three key factors – cost, time, and innovation – and illustrates how integrating these factors will minimise the challenges and ensure success in outsourcing drug discovery.

Figure 1. Three common questions regarding the challenges when considering outsourcing partners for drug discovery.

Opportunities, options, and outsourcing

During a 2021 webinar on optimising assay development for hit identification, a survey asked over 50 drug discovery scientists, “Which goal is the highest priority when developing a drug discovery assay?”

  • Generating robust, reproducible data generating confidence in results
  • Completing the project by a defined deadline
  • Minimising reagent consumption and costs
  • Having follow-up assays available for downstream analysis

The overwhelming response, not surprisingly, was reproducible, robust data1. Interestingly, minimising costs received zero votes. Yet, when it comes to outsourcing projects to CROs, the mindset changes, and the importance of costs receives significant attention. In other words, scientists do want results better AND faster, but also at the lowest cost.

Comparing costs for outsourcing versus the costs of running programs in-house is not always clear and requires considering more than just the cost associated with a proposal. Consider Company “Z”, a biotech company ready to initiate hit finding efforts on their novel target. The company has the resources to complete the project in-house or it could outsource it to CROs. The company has narrowed down their decision to three options:

Option #1: Company Z performs the project internally and screens their in-house collection of 250,000 small molecules using a traditional fluorescence-based biochemical assay. The fluorescence method is high-throughput and kits are commercially available. Company Z identified a team of three scientists to run the project from start to finish estimating a few months to lead candidates.

Option #2: As a budget-friendly and rapid strategy, Company Z outsources a virtual screen of millions of commercial compounds to a CRO that specialises in artificial intelligence (AI) and machine learning software to predict compounds that bind to a target. Company Z would generate candidates in weeks rather than months required with Option #1.

Option #3: Understanding the caveats of traditional label-based assays, Company Z is considering outsourcing their project to a CRO that specialises in mass spectrometry approaches for assay development and hit-finding. Company Z would have to rely on the CRO for guidance and expertise. The assay may be the most innovative and would generate leads in weeks, but at slightly higher costs than traditional assay methodologies. For Company Z to make their decision, they must integrate the costs, time, and innovation opportunities and determine which option offers the highest likelihood of success.

Key #1: Hidden costs or hidden value?

Consider the process for purchasing a new car where one car on the lot may come at a higher price. This car is known to be more reliable, requires less maintenance, and is more fuel efficient than the less expensive option. Clearly, the more expensive car offers significantly more value and the less expensive car comes with hidden costs such as routine required maintenance. Yet, it is difficult for many people to appreciate the forward-looking decision and they often opt for the instant savings.

For many scientists, understanding the costs and value for various projects is similarly challenging. For Company Z, the first option to run the project in-house may appear to be the lowest cost option since the outsourcing budget is essentially zero. But how much value will they generate with this option versus outsourcing? To address this question, the table below outlines the hidden costs and hidden value for each option. We note that time is one of the most valuable commodities, and therefore it is discussed separately in the next section.

The assessment of costs versus value reveals many aspects that are often overlooked by scientists, especially the cost for rent, and lost opportunities due to the use of internal resources on projects that could be outsourced. Additionally, the lower cost options may come with more expensive follow-up, supporting that it is more important to understand the total costs from project initiation to lead compound rather than the cost to generate hits alone. Finally, the value from having confidence in hits cannot be underestimated.

Key #2: Time is of the essence

With an understanding of costs and value, the second key factor to consider is time. Time is one of the most valuable commodities and while it is common for scientists to ask outsourcing partners, “how long until I see my data?” the true value of time is not always appreciated. Imagine that Company Z develops a blockbuster drug that generates $1 billion per year. Once that compound is identified and the IP filed, the clock starts on IP protection. Each week that goes by without data could cost the company more than $1M in lost revenue on top of daily expenses2. That may sound overwhelming or even hyperbole, and many scientists may relate more towards the aspects of time focused on the drug discovery project alone. Company Z must ask questions to assess the four stages of their drug discovery project:

  •  How long will it take to develop an assay suitable for screening?
  • How much time to prepare sufficient materials and reagents for screening?
  • How long for the hit identification process?
  • How much time to validate hits and achieve that first milestone?

In asking these questions consider that internal resources may also have other commitments that delay progress. For example, internal schedules and screening resources at larger pharma are often planned months if not years in advance, so slotting a project into an existing pipeline poses its own timing challenges. Conversely, CROs can dedicate as many resources as needed to complete projects rapidly while maintaining quality data. Validation from label-based or virtual efforts will require considerably longer timelines than label-free counterparts (Figure 2).

Figure 2: Thought experiment: Estimated timelines for three drug discovery options. Options 1 and 2 may ultimately require the longest timeline towards lead generation while option 3 may offer the most accelerated path.

Key #3: How innovation drives success

Innovation in assay development and screening has opened new avenues to tackle challenging, and previously intractable, therapeutic targets. Many CROs are driving innovation in this space through new instruments with higher sensitivity, faster read times, and with new drug discovery tools to augment assays and data analysis. These innovations are working to bring the future of drug discovery to today’s labs and creating new opportunities for biopharma companies such as Company Z.

Innovative assays, new solutions

Many traditional drug discovery assays still rely on labels – optical or radioactive – to provide a readout associated with functional activity or a binding event (e.g. protein-protein or small molecule-target interactions). The caveats of labeled assays are well-established, including high rates of false positive and false negative results from optical interference, costly reagents, and extensive follow-up to identify the most promising leads. Label-free approaches, particularly those integrating mass spectrometry (MS), deliver reliable data by informing on the mass ID of the analytes rather than relying on interpretation of a signal3. Accessing these innovative tools, which requires dedicated (and often expensive) instrumentation and trained staff, occurs through specialised CROs. The initial costs for innovative technologies may appear higher than traditional approaches or running programs internally. However, considering the additional expenses and time needed for reagents and validation assays, for example, innovative technologies may offer significant cost and time savings in addition to generating higher confidence in the results. Finally, computational tools integrating artificial intelligence (AI) and machine learning are increasing opportunities to improve hitto-lead optimisation, with aims to improve potency, selectivity, and biological impact in a rapid and cost-effective manner. The most important aspect is understanding how to interpret and utilise the data to progress to next steps.

Compound libraries: Small molecules, large roles

The small molecules to be screened represent a significant resource of any drug discovery program. In the last decade, advances in chemical synthesis capabilities and our understanding of chemical matter have led to innovations in preparing quality small molecule libraries. Big pharma companies routinely maintain their own decks of upwards of millions of compounds. However, the majority of drug discovery companies either do not own a compound library or maintain a focused set of compounds for their targets of interest. Therefore, small molecule libraries are selected from compound vendors or screening CROs.

Selecting compounds may focus on compound diversity or there may be options for focused libraries with distinct criteria and may depend on the assay methodology and target. Some CROs may not have freedom to operate (FTO) with certain targets due to exclusivity policies, and thus, alternative CROs or solutions are required. Specialty libraries, such as those used with DNA-encoded libraries (DELs) – where small molecules are synthesised with DNA probes – offer a method to screen millions to billions of small molecules and identify binders based on DNA sequence identifier. Designing DEL libraries has benefited from the development of AI to model compounds based on binding pockets. Alternatively, innovation with MS techniques has offered high-throughput binding assays – an approach called affinity selection mass spectrometry or ASMS4 – that reveals compound based on mass rather than a DNA sequence, eliminating artifacts from interference of DNA molecules with targets.


Selecting the optimal path for generating quality drug leads is a critical decision for any company. Considering a variety of options will present opportunities and identify potential challenges. It is important to evaluate costs, time, and innovation with respect to overall value when evaluating where and when to run hit finding efforts, including through unique solutions offered by CROs.




3: McLaren DG, Shah V, Wisniewski T et al., High-throughput mass pectrometry for hit identification: current landscape and future perspectives, 2021, SLAS Disc, 26, 168-191.

4: Scholle MD, McLaughlin D, Gurard-Levin ZA, High-throughput affinity selection mass spectrometry using SAMDI-MS to identify small-molecule binders of the human rhinovirus 3C protease, 2021, SLAS Disc, 26, 974-983.

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