Automating Drug Discovery’s Next Breakthrough

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Drug innovators’ ability to create novel life-changing pharmaceuticals relies heavily on a diligent, sophisticated and multidisciplinary discovery process. Scientific advancements in the understanding of the human body and diseases, along with continued adoption of breakthrough technologies like high- throughput screens (HTS), have dramatically trans- formed the landscape of modern drug discovery. Novel science and technologies are constantly reshaping this field with exciting, innovative research ideas and discovery tools.

High-throughput Screens and Laboratory Automation

Drug discovery starts by selecting a validated biological target, typically a gene or a protein underlying the disease being studied. The groundbreaking work in understanding the pathogenesis of diseases at a molecular level is often accomplished via academic research laboratories and then published to enrich public scientific knowledge. Once a target is validated, the search begins for a lead compound — either an organic compound or other drug molecule — which can interact with the tar- get and modify its function. Ideally, the lead molecule will alter the disease course without affecting any off- target molecules. This process involves the generation of lead compounds and cycles of lead optimization, pharmacokinetic profiling and toxicity testing. ¹

High-throughput screens, a critical element of modern drug discovery, are playing a major role in identifying lead compounds. Introduced to the dis- cipline in the mid-1980s, this innovative technology was expanded significantly in the 1990s with an array of technological innovations that allow HTS to screen a large number of compounds (millions) against the drug target in a timely and cost-effective manner.

This array of innovation includes parallel synthesis/combinatorial chemistry (the technique for rapid generation of every possible variant of a parent com- pound, physically or virtually); automated high-perfor- mance liquid chromatography to purify products of combinatorial synthesis; and especially lab automation to improve the efficiency of HTS and streamline the drug-discovery workflow.

With the aid of automated technology and equipment, screening millions of compounds for leads becomes attainable and economical. Lab automation accelerates the speed of performing large-scale sample analysis with a high degree of reproducibility and accuracy, as well as eliminating some of the tedium of manual lab work. A broad range of routine laboratory procedures, such as chromatography, mass spectrom- etry, and DNA and peptide synthesis, can be conducted by semi- or fully automated instruments. ²

In addition, automation is critical in achieving assay miniaturization. This has become an important feature of HTS in response to the increasing number of chemical compounds and molecular targets. Miniaturized assays, such as microarrays, shorten the throughput screen time by using small volumes of samples and reagents, while improving screen efficiency and reducing costs.

This technology requires precise liquid handling within the range of microliters or even nanoliters. Through the facility of automation, such small- quantity liquid measurement and dispensing can be accomplished by advanced liquid-handling instru ments using robot arms. Some of these instruments offer real-time dispense verification and independent pipetting control, ensuring precise and accurate liquid dispensing. They can also function as an integral part of a fully automated system. ³

All About Eve

An emerging trend in lab automation is to fully automate drug-discovery workflows through “robotic researchers” that apply advanced machine learning and artificial intelligence. One such system is Eve, a collection of computers connected to instrument automation. Eve combines three separate parts of the drug-screening pipeline into a systemic and integrated process, thus streamlining compound screening, hit validation and analysis. Eve’s developer, professor Ross King (University of Manchester, U.K.), believes that the tool will have a lasting influence on the efficacy of drug discovery, given its ability to intelligently respond to a hit with instantaneous analysis rather than after the screening. ⁴

The demand for high testing accuracy and reproducibility is the main driver for a robust drug-discovery laboratory automation system market ($4.1 billion in 2014). As well as large testing volumes and new drugdiscovery technology adoption, laboratory automation systems cover almost every aspect of the discovery process, including liquid handling, plate readers, dissolution testing, storage retrieval, laboratory information management systems and robotic systems. ⁵

Computer-aided Drug Discovery/design (CADD)

In addition to the traditional experimental approach, computer-aided drug discovery (CADD) forms an important branch of modern drug discovery and is broadly used to facilitate and expedite hit identification, lead selection and optimization. Availability of a variety of databases (i.e., chemogenomics, pharmacogenomics, protein data banks and therapeutics target databases), improved computer processing power and information technology are fundamental to the CADD approach.

CADD can be classified into two broad categories. The first is the structural-based design of new molecules centered on the desired binding to a target. This approach applies the 3D structural information of a target (i.e., a protein) to reverse engineer suitable binding molecules through simulated docking. The other is ligand-based design, which focuses on developing new molecules based on known active or inactive ligands against a target through ligand chemical-similarity modeling. ⁶ A variety of algorithms can be used to facilitate these two approaches.

The most common use of CADD is to perform virtual high-throughput screening over simulated compound libraries by structure-based, ligand-based or combined methods. ⁷ Another important application of CADD is de novo drug design, in which novel compounds are developed from starting molecules with demonstrated activity by adding one functional group at a time or piecing together fragments into novel chemical entities using construction algorithms. In addition, comprehensive algorithms have been used to predict a drug’s ADME (absorption, distribution, metabolism and excretion), related properties and potential toxicity. MetaSite software for example (Molecular Discovery Ltd., Middlesex, U.K.), also offers in silico structure modification to improve the metabolism profile of the lead compounds. ⁷

Precision Research Models for Better Out Come Prediction in Humans

One challenge facing drug discovery is to predict an investigational drug’s pharmacological and toxicological behavior in humans based on the results of in vitro and animal testing. To increase effectiveness, drug innovators must improve their ability to predict failure and reject drug candidates as early as possible.

According to a study led by the Director of Economic Analysis at Tufts Center for the Study of Drug Development, Dr. Joseph A. DiMasi, the success rate of a drug from phase I to market approval is approximately 11.83%. ⁸ Unacceptable toxicity, lack of desired result and disappointing pharmacokinetics (i.e., ADME) are the main reasons for drug failure. ⁹ Just a 10% improvement in predicting failures before clinical trials can translate into savings of up to $100 million on development costs. ¹⁰

To meet this challenge, precision research animal models are created by inserting human genetic components into an animal model or by engrafting human cells, tissue or tumor cells to the immunodeficient mice to mimic human organ systems or diseases. ¹⁰ Due to their “humanized” features, these models ad- dress the species-difference issue that plagues traditional animal models, improving the reliability in predicting human outcomes with respect to effectiveness and safety. Precision research models are commonly employed to mimic an array of human disorders. ¹⁰ The hPXR/CAR/CYP3A4/2D6/2C9 mouse is currently the most genetically humanized model available, with 33 human genetic counterparts substituting the mouse’s own genes. This model is used in predicting induction and inhibition of human cytochromes and drug-drug interactions. ¹¹

Another exciting advancement in this area is the emergence of 3D bioprinted human tissue models. The leading technology, exVive3D™ , was developed by Organovo for preclinical testing and drug discovery research. ¹² The company’s first commercial product — exVive3D Human Liver Tissue — is generated by depositing groups of patient-derived living cells in precise layers by a 3D printer. One significant advancement offered by the living 3D liver tissue is a longer functional and stable period compared to standard 2D liver cell cultures. ¹³ Unquestionably, the 3D bioprinted human tissue models are powerful research tools to assist in understanding a particular disease and treatment.

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