Do you observe how machines and artificial intelligence solve half your problems by helping you with your daily tasks? This goes for labs too, especially when designing hundreds of experiments, documenting each step, running them in parallel and collectively assessing their results. Automation of certain or all (if possible) stages saves time in completing many tedious tasks that would otherwise have been done manually.

Labs equipped with high-throughput equipment and tools, running on smart software, can scale their R&D and manufacturing output.

Using automation can enable pharmaceutical companies to make better decisions and produce quality and effective therapeutics, especially in drug discovery and manufacturing.

Laboratory environments have evolved remarkably due to rapid technological progress. They are now able to manage vast amounts of repetitive tasks easily and substantially improve the reliability, accuracy, efficiency and consistency of end results.

In this article, we further delve into the stages of lab automation, how it can benefit your lab, the challenges you might face, and learn how automation is going to lead the future of drug discovery.

Benefits of Lab Automation in Drug Discovery

• Increase efficiency and productivity: Lab routines often involve many mundane tasks. Automation and robotic systems can help labs of any size automate repetitive tasks and enhance the efficiency and productivity of the lab. They can remove the fatigue factor from performing manual tasks and increase productivity by allowing labs to scale operations or extend operational hours.
• Enhance data quality and accuracy: Experimental variability is inevitable in manual processes like pipetting, preparing solutions, labeling, barcoding, or documenting results. Automation significantly reduces variability which increases data accuracy and quality. It also helps increase the rate of data capture which can provide additional data points for consideration.
• Ensure traceability: Integrating laboratory information management systems (LIMS) and other enterprise level software platforms can automate data collection to provide complete traceability of the sample’s journey, enabling seamless tracking from collection to disposal.
• Provide safety: Automation and robotics can minimize exposure to biological agents and contaminants for workers and samples. They can even lessen the likelihood of workers suffering repetitive strain injuries caused by performing manual work.
• Accelerate drug discovery process: Reducing training requirements for new personnel with intuitive automation, time for experimental setup, or manual processes associated with data acquisition and analysis all contribute to a speedier drug discovery process. Laboratory and data automation present researchers the ability to streamline their processes and focus on moving lead candidates to the clinical faster.

What is the difference between partial automation and total workflow automation?

Partial automation of lab processes involves automating specific tasks with standalone equipment, but it still requires human intervention and lacks integration. In contrast, total workflow automation integrates multiple processes and workflows, including data management, enabling end-to-end automation to improve overall efficiency.

Applications of Lab Automation in Drug Discovery

Medicinal Chemistry

Automation is crucial in medicinal chemistry for synthesizing and purifying compounds. It enables the efficient production of diverse compound libraries, facilitating drug discovery. Automated systems can also perform combinatorial chemistry, accelerating compound synthesis.

Compound Management

It involves the storage, retrieval and tracking of chemical libraries. Automated systems store thousands or even millions of compounds in a well-organized manner. Further, they also help maintain accurate inventory records, ensuring the integrity of the compound collection.

Assay Development and Optimization

Automated systems play a key role in assay design and implementation. They facilitate the optimization of assay conditions, such as concentration, incubation time, and temperature, through real-time analysis to enhance the sensitivity and reliability of the assay results.

High-Throughput Screening (HTS)

High-throughput screening is a crucial step in drug discovery, where large compound libraries are screened against specific biological targets or disease models. Automated systems enable the rapid screening of thousands to millions of compounds, accelerating the identification of potential hits. These systems perform the screening process consistently and efficiently, increasing the chances of discovering promising lead compounds.

ADME-Tox Profiling

In silico ADME/Tox profiling helps predict the pharmacological and toxicological properties of drug candidates, particularly during preclinical development. Automation of the process enables the rapid and reliable profiling of compounds. Further, predictive models and screening tools, integrated within automated data systems, help in interpreting and analyzing the ADME-Tox data.

Report Generation

Drug discovery requires reports to be generated for eventual regulatory filing. Automated data management platforms can collate and curate the data in required formats for regulatory filing. They can even expedite the tech transfer process by formatting data packets to be handed off to the next department or entity.

Challenges and Considerations in Drug Discovery

Despite its numerous benefits, automation of labs faces several challenges:

• Skills gap and loss: Total lab automation is still a relatively recent concept. Thus, lab personnel must be well-trained in integrating high-throughput equipment with advanced software. However, it can still be challenging for labs to keep up with the knowledge gap when a new system is released, or the old system is phased out which necessitates an upgrade.
• Learning curve: Adopting new technology requires a learning curve for lab technicians to optimize their proficiency, especially when transitioning to automation and software management. This presents challenges as workflows change and new skills are required for various tasks.
• High short-term costs: Implementing automation involves initial costs for project accommodation, system installation and new hardware. Facilities may need to negotiate the return on investment and establish a business case for leadership before automation capital expenses are considered.
• Infrastructure constraints: Implementing automation in existing laboratories can be challenging due to space and infrastructure constraints. Some advanced automation systems require more infrastructure, wiring and facilities to function effectively.
• Validation: When changing process steps or equipment, it is important revalidate the process. This can be an arduous process for some labs and constitute a resource drain. Depending on the size of the operation or scope of the changes, revalidation could take weeks to months and hinder output.

The Future of Laboratory Automation

Lab automation has significantly improved laboratories and revolutionized the drug discovery process by enhancing efficiency and speed while reducing errors. The latest buzz in automation is Intelligent Process Automation (IPA), where cutting-edge technologies like smart workflows embedded with artificial intelligence and machine learning come together to deliver remarkable capabilities.

Another area of focus is miniaturization and microfluidics, which enables lab-on-a-chip technologies. These microscale platforms offer efficient sample handling and precise control of fluid flow on a single chip. Such platforms are constantly emerging as powerful tools for high-throughput screening, providing faster results, reduced reagent consumption and improved cost-effectiveness. They also lower the infrastructure requirements and overall experimental costs by reducing reagent and compound volumes.

Sustainability is at the forefront of drug developers and lab automation producers. Pledges to reduce company greenhouse gas emissions and carbon footprint are influencing automation design especially for workflows or applications tied to high use of plastics.

With the continuous evolution of these technologies, the future of lab automation holds great potential for enhanced efficiency, accelerated results and expanded avenues for scientific exploration. By adopting these advancements, researchers can unlock new frontiers and push the boundaries of scientific discovery, opening exciting possibilities for the future.

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