Drug Discovery

Jump to: Drug Discovery Process |Drug induced kidney injury | Drug-membrane interaction | Drug-drug interactions | Chemoinformatics


Drug Discovery Process

Drug Discovery
Modern drug discovery seeks to identify novel small molecules that effectively and selectively modulate a disease related function. In the past, nature provided the majority of such novel molecules, but in the last 30 years synthetic compounds have become the basis of compound screening sets. As a consequence, the size of such libraries is typically in the order of thousands to millions of molecules, which requires highly efficient screening technologies and efficient analysis and decision-making methodologies.

Such screening-based drug discovery approaches can start from an identified target, in which case a target-based screening assay needs to be developed, which usually involves the testing of small molecules against a purified protein. Recent advances in screening technologies have led to the (re)emergence of cell- or organism-based phenotypic assays, in which the effect of small molecules are investigated in the cellular context, rather than on isolated components. Depending on disease and target context both approaches have their advantages and disadvantages.

Target Validation
For target-based screening approaches Target Validation is an important step in the drug discovery and development process. Target validation investigates which type of molecules are able to modulate the target and if the target modulation has an effect on the desired phenotype or disease state. Target druggability is usually linked to the likelihood to find a small molecule with high affinity binding to a given protein [1]. However, the concept can be expanded to the likelihood of a high affinity binder actually reaching the target in the cell, and to the likelihood of a high affinity binder to be specific enough to have the desired activity without displaying adverse effects.

The Cooper Group applies several predictive tools to evaluate the druggability of a target, including structural binding pocket evaluation combined with bioinformatics evaluation of how conserved or variable the potential pocket is between isoforms of the target, within the normal population of the host, and throughout the evolution. The Cooper Group is equipped with a range of in silico methodologies and technologies, including in-house developed workflows.
The Cooper Group also uses its medicinal chemistry capability to develop tool compounds which can be used to investigate Target Validation in various cell-based and in vivo studies. Tool compounds usually constitute drug candidates with limited potential, either due to limited IP protection or limited application in humans. Despite their reduced potential as final drug candidate they represent a valuable tool for early drug discovery, early target validation and proof-of-concept studies. We are currently using such tool compounds to investigate the druggability and target validation of developmental transcription factors in the treatment of solid tumours, and intra cellular and membrane-bound receptors for the modulation of inflammatory responses.

Target Identification and Mode of Action
For phenotypic screening approaches Target Identification and Mode-of-Action (MoA) studies are important for the validation and progression of drug leads. Detailed information on the molecular and structural biology on how an active compound modulates a phenotype is crucial to the design any lead molecules with improved activity and improved ADME/Tox properties. Several approaches are usually applied for target identification, such as direct biochemical methods, genetic interaction methods and computational inference methods [2]. Direct methods involve labelling of protein, ligand/inhibitor or both, and detection or separation of the labelled molecules. Genetic manipulations involve the modulation of presumed targets, ether as knock-outs or inactive mutations, and investigate changes in the sensitivity of the small molecules. Computational approaches use pattern recognition or similarity algorithm to infer possible target by similarity to known targets and/or known bioactives.  

The Cooper Group uses its medicinal chemistry capability to develop labelled small molecule probes, by adding immune-histological or fluorescence tags to known active molecules. A large range of molecular probes have been developed for antimicrobial targets [3], as well as anti-inflammation targets. The Cooper Group also uses regularly genetic methods to generate mutants, as well as transposon-directed insertion site sequencing (TraDIS) methods [4] to investigate resistance mechanisms of bacteria against anti-microbial. In addition, the Cooper Group applies a range of computational methods for protein and chemical similarity analysis [5].

Lead Discovery
The outcome of any screening based drug discovery process depends largely on the selection of the set or library of small molecules going into a screening campaign. Libraries are either selected to represent diversity, exploring chemical and structural variations, or are focused, exploring the structure-activity relationship around hits. However, as most compound library becoming more and more hydrophobic [5], the selection also requires to consider physico-chemical properties of the compounds not only to aim for good oral-bioavailability (or drug-likeness), but also to minimize screening errors due to low solubility or promiscuous binding.

The Cooper Group applies several in silico methods, for optimal selection of screening libraries, including chemo-informatic selection of diverse library with desirable properties and virtual screening for focused libraries [6, 7]. The Cooper Group has access to a wide range of commercial and open-source software, including an extended database of compounds, commercial and bioactives, curated and maintained specifically for our drug discovery projects.

Lead Optimization
The next step in the drug discovery process is the optimization of hits from the screening into lead candidates. Chemical modifications are applied to the hit structures to improve their efficacy while reducing any toxicity or adverse effects the small molecules might have. Various methods are applied to guide the chemical modifications, using structure-activity relationship analysis from a set of active ligands, using structure information of the target to improve activity, or applying medicinal chemistry guidelines to improve pharmaco-kinetic properties such as stability, half-life and distribution.

The Cooper Group uses chemo-informatic and machine learning tools to build predictive model for structure-activity relationship, which are applied for the selection of screening libraries, as well as optimization of the hit structure for improved activity. The Cooper Group uses its in-house databases of compounds and bioactivity data, which the group maintains and curates. The Cooper Group also has access to the in-house protein crystallisation facilities which are used for co-crystallisation experiment of the protein with the active small molecules. These facilities are compensated with high field NMR facilities and molecular dynamic simulations to investigate the dynamic behaviour of the ligand-target interaction, including interaction of small molecules with membranes of various species (i.e. bacterial vs. mammalian).

The medicinal chemistry facilities in Cooper Group are able to take the concept of the design and implement the chemical modifications not only for discovery but also for in vivo studies (i.e. larger scale). The Cooper Group has several antibacterial leads currently in animal studies.  

Key publications
1.            Cheng, A.C., et al., Structure-based maximal affinity model predicts small-molecule druggability. Nat Biotechnol, 2007. 25(1): p. 71-5.
2.            Schenone, M., et al., Target identification and mechanism of action in chemical biology and drug discovery. Nat Chem Biol, 2013. 9(4): p. 232-40.
3.            Phetsang, W., et al., An azido-oxazolidinone antibiotic for live bacterial cell imaging and generation of antibiotic variants. Bioorganic & medicinal chemistry, 2014. 22(16): p. 4490-8.
4.            van Opijnen, T. and A. Camilli, Transposon insertion sequencing: a new tool for systems-level analysis of microorganisms. Nat Rev Microbiol, 2013. 11(7): p. 435-42.
5.            Zuegg, J. and M.A. Cooper, Drug-likeness and increased hydrophobicity of commercially available compound libraries for drug screening. Curr Top Med Chem, 2012. 12(14): p. 1500-13.
6.            Karoli, T., et al., Identification of antitubercular benzothiazinone compounds by ligand-based design. J Med Chem, 2012. 55(17): p. 7940-4.
7.            Karoli, T., et al., Structure aided design of chimeric antibiotics. Bioorg Med Chem Lett, 2012. 22(7): p. 2428-33.


Drug Discovery - Cooper Group Research

Drug induced kidney injury

Nephrotoxicity, or damage to the kidney, is a side effect of many marketed drugs, with 19-25% of acute renal failures caused, in part, by drug exposure [1]. Many of these drugs are antibiotics, and include a number of classes that are the focus of drug discovery projects within the group. In the search for improved versions of existing drugs, and for the development of novel therapies, it would be very helpful to be able to screen for potential nephrotoxicity. Unfortunately, the gold standard for preclinical compound testing is kidney histopathology from animal studies, a low throughput and expensive procedure that requires sacrifice of the animal. In vivo monitoring of serum creatinine (SCr) or blood urea nitrogen (BUN) levels provides an alternative readout to kidney biopsies, but sensitivity and correlation to injury is poor. There has been an intensive effort in recent years to identify in vivo biomarkers that can be used to selectively monitor kidney damage [2-8]. In particular, proteins such as NGAL (Neutrophil gelatinase-associated lipocalin) [9] and Kim-1 (Kidney injury molecule-1) [7, 10] have been highlighted as proteins with much more relevance to early detection of kidney injury than traditional serum creatinine levels.

For drug discovery screening, a cell-based assay is much more useful than an in vivo assay, as potential liabilities can be assessed at a much earlier timepoint, and structure-activity relationships can be explored at a reasonable cost. There have been numerous research reports on the use of cellular assays systems to detect nephrotoxicity. However, the scope of these studies has been restricted by either the types of cells employed, the toxicity readouts assessed, or the drugs applied to the cells. Furthermore, the possible in vitro utility of many of the potential new in vivo biomarkers requires investigation.

We are undertaking a comprehensive research program using a matrix of cell types, reference compounds, and readouts to identify in vitro cellular assays that can be used to predict nephrotoxicity. We will investigate multiple human kidney-related cell types and compare the results to those seen in non-renal cells, allowing for the differentiation of general cytotoxicity from nephrotoxicity. We will test a range of known nephrotoxic and cytotoxic reference compounds. The cellular effects will be monitored using general cytotoxicity assays, biomarker-based assays, high content screening using fluorescent labelled markers, and label-free assays using both optical and electrical impedence biosensors. The results will provide us with an ability to counterscreen the group’s antibiotic drug discovery efforts for nephrotoxic side effects. In the future we hope to expand this effort to all drugs as a tool for the scientific and pharmaceutical research communities.

Figure 1. (a) Biomarkers of kidney injury and (b) Drugs that elicit site-specific toxicity [1]


[1] Bonventre, J.V.; et al., Nat. Biotechnol. 2010, 28, 436.

[2] Ozer, J.S.; et al., Nat. Biotechnol. 2010, 28, 486.

[3] Mattes, W.B.; et al., Nat. Biotechnol. 2010, 28, 432.

[4] Sistare, F.D.; et al., Nat. Biotechnol. 2010, 28, 446.

[5] Dieterle, F.; et al., Nat. Biotechnol. 2010, 28, 455.

[6] Dieterle, F.; et al., Nat. Biotechnol. 2010, 28, 463.

[7] Hoffmann, D.; et al., Toxicol. Sci. 2010, 116, 8.

[8] Yu, Y.; et al., Nat. Biotechnol. 2010, 28, 470

[9] Paragas, N.; et al., Nat. Med. 2011, 17, 216.

[10] Vaidya, V.S.; et al., Nat. Biotechnol. 2010, 28, 478.

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Drug-membrane interaction

All living cells are surrounded by one or more membranes. These membranes, composed of lipids and proteins, play important roles in cell survival and function. Drug design generally focuses on the interactions between ligands and their receptor or enzyme targets, and largely ignores the role played by cell membranes, particularly for membrane-based protein targets. However, knowledge of drug-membrane interactions is essential for understanding a drug’s biodistribution, activity, selectivity and toxicity.

The development of analytical tools for the study of drug-membrane interactions of increasing interest to scientists. Methods such as high-performance liquid chromatography (HPLC), fluorescence techniques and NMR are commonly used. We are undertaking several projects using surface plasmon resonance (SPR), cell impedance and resonant waveguide photonics; all label-free techniques, to investigate the interactions between novel antibiotics and cell membranes [1-5]. These provide an ideal model of cell membrane that can be varied to determine the binding affinity and kinetics of drugs on different membrane types. The results not only provide valuable information for drug design and development, but also contribute to investigations into the mode of action of novel antibiotics and cancer therapeutics.


[1] Cooper, M. A.; Williams, D. H., Chem. Biol. 1999, 6, 891.

[2] Cooper, M.A., Label-free biosensors : techniques and applications. 2009 Cambridge University Press: Cambridge.

[3] Chia, C. S.; Gong, Y.; Bowie, J. H.; Zuegg, J.; Cooper, M. A., Biopolymers. 2010, doi: 10.1022/bip.21438.

[4] Nussio, M. R.; Sykes, M. J.; Miners, J. O.; Shapter, J. G., ChemMedChem. 2007, 2, 366.

[5] Cooper, M.A., J. Mol. Recognit. 2004, 17, 286.

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Drug-drug interactions

With the general population now often taking more than one drug at the same time, adverse effects of drug-drug interactions are becoming very prominent. Knowing if there are side effects and changes in pharmacokinetics of a drug as a result of taking two different drugs at one time, is critical for drug efficacy. Pharmocokinetic interaction between drugs arise thereby if one drug changes the absorption, distribution, metabolism, or excretion of another drug, changing the concentration of active drugs in the body, in some case above their maximal tolerable dose.

Several systems are involved in changing the pharmacokinetic properties of a drug, including Cytochrome P450 and residence time on serum albumin. Inhibition of the CYP450 of one drug can change the metabolism of a second drug, whereas competitive binding to albumin affects the concentration and half-life of drugs in the blood. This research project is focusing on the development of fast, high throughput screening methods to investigate drug-drug interactions.

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Chemoinformatic methods, generally described as computer and informational techniques in the field of chemistry, are nowadays essential tools in the discovery and development of new active compounds. In combination with databases such as chEMBL or PubChem, they provide essential information about existing compounds and compounds classes. But more importantly, using various statistical and predictive modeling tools Chemoinformatic methods are able to provide prediction on on- and off-target activity/selectivity and biochemical and pharmacokinetic properties (ADME-Tox)

The research project aims to develop and implement a comprehensive toolset and database for chemoinformatic. The main focus of the project will be the integration of molecular modelling (structural biology) and bioinformatic (molecular biology) methods with the chemoinformatic (chemistry) toolset.

For example, drug-target networks combine the sequence similarity between targets with the chemical similarity of their targets, to establish a wider relationship between structures and targets than would have been possible using only a single similarity relationship. Similarly, integrating structure docking and target homology procedures into the chemoinformatic toolsets will enhance its functionality.

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