**2. HTS in drug discovery for infectious diseases**

Infectious diseases arise in any person due to certain microbes which enter the body and multiply to give clinical symptoms of the disease. While some infections are contagious and spread from one person to another person, others may spread in the community through infectious vectors (insect/animal bites) or contaminated air, water, and food [6]. These microbes undergo mechanisms of resistance to antibiotics either under the direct influence of antibiotics or through adaptive processes unrelated to the chemical structures of antibiotics [7, 8]. The increase in the number of antibiotic-resistant microbial strains makes it evident to discover and develop newer efficacious drugs. However, developing a new drug is a tedious and complex process with uncertain outcomes; therefore, the process needs to be rational in approach. HTS offers a highly rationalized automation approach to explore large chemical space in a time-efficient manner. However, it requires complex and costly technological platforms which are generally available in pharmaceutical companies [4]. Nevertheless, it is not expensive because HTS screens a huge number of chemical compounds as compared to manual methods for target-to-lead discovery. An overall success rate of HTS to find leads is considered ⁓50%. However, vHTS is considered

*High-Throughput Screening for Drug Discovery toward Infectious Diseases: Options and Challenges DOI: http://dx.doi.org/10.5772/intechopen.102936*

to have a higher success rate, but every method has its strengths and weaknesses, and therefore both the methods, HTS and vHTS, should be coupled for lead discovery. Few examples, among successful HTS drug discoveries in anti-infective agents, are (i) G-protein-coupled receptor (GPCR) inhibitor, Maraviroc (anti-HIV), (ii) reverse transcriptase (RT) inhibitor, etravirine (anti-HIV), [9] and (iii) hepatitis C virus (HCV) genotype 1a/b or 3 RNA replication inhibitor, Daclatasvir [10].

#### **2.1 Need of HTS in drug discovery for infectious diseases**

Bacterial enzymes play a significant role in developing antibiotic resistance through several key mechanisms and genetically derived mutations happening in: (i) drug-modifying enzymes (such as *transferases* and *hydrolases*), (ii) drug-metabolizing enzymes (such as *pyrazinamidase*, *catalase-peroxidase*, and *monooxygenase*), (iii) antibiotic's target enzymes (such as *RNA-dependent RNA polymerase* (RdRp) and *Topoisomerase II*), and (iv) antibiotic's cellular target-modifying enzymes (*rRNAmethyltransferases* and *phosphoethanolaminetransferase*). The structural changes in these enzymes not only lead to resistance among microbes but also open the 'omics gates to identify newer targets that originated after modifications in enzymes [8–10]. The rapid spread of resistance among microbes makes it imperative to rapidly identify new classes of antibiotics. Traditionally, growth inhibition assays are used for antimicrobial drug discovery which is a slow process [11, 12]. However, to match the pace of microbial resistance to antibiotics, a robotic automation screening process with efficient, accurate, and robust scientific methodology is required. HTS offers an economic advantage of screening huge chemical spaces accurately within defined timelines. Therefore, time, cost, and quality are termed as the "magic triangle of HTS" [13]. The credit of rapid HTS goes to: (i) high-density arrays, micro-reaction wells, and (ii) biological response detection methods.

High-density array micro-reaction well plates ranging from 96-well plates to miniaturized 3456-well plates are available with typical working volumes ranging from 1 to 10 μl of total volume. However, efforts are being made for further miniaturization of plates [13] to develop mega-dense arrays (>10,000 wells/plate) [14]. Although there are few difficulties associated with ultra-high-density plates, nevertheless, it is possible to perform 100,000 assays per day using ultra-highthroughput screening (uHTS) [15].

Biological response detection techniques such as fluorescence, luminescence, and atomic absorption spectroscopy have been established, which makes the process robust in the identification of active compounds. These direct and indirect detection methods have been developed based on: (i) direct measurement of absorbance and (ii) indirect measurement through enzymatic or chemical reactions coupled with pH indicators and chelators. These methods establish a quantitative relationship between biological response and target metabolite concentration [16]. Apart from the pharmacological aspect, HTS is equally beneficial in the evaluation of toxicological aspects of the chemical entities such as (i) genotoxicity, (ii) carcinogenicity, and (iii) immunotoxicity [15].

Plants have an abundance of potentially diverse chemical entities in the form of complex mixtures which are required to be evaluated in HTS for the discovery of new drugs against microbes. However, pure chemical entities from these complex mixtures need to be isolated and structurally characterized before proceeding for target-specific evaluation. Bioassay (*in vitro*)-guided HTS of these plant extracts aids in the identification and isolation of bioactive compounds (**Figure 1**) [17].

Similarly, vHTS is a bioethical approach consisting of a wide variety of *in silico* simulation approaches to explore chemical libraries and identify which chemical

entity has the potential to display *in vitro* and/or *in vivo* drug-like properties in HTS. However, there are chances of false-negative and false-positive results [4].

### **3. Methods involved in HTS for drug discovery toward infectious diseases**

#### **3.1 Classification of HTS**

HTS methods for anti-infective drug discovery may be biological (cell-based or whole organism), biochemical (enzymes/receptors), and virtual (computer-based). Hence, the HTS methods may be classified as summarized in **Figure 2**. The HTS assay approach for the identification of "lead" molecule may vary depending on the target; however, the assay protocol must be (i) sensitive to low potency molecules, (ii) reproducible in biological response, (iii) accurate in terms of positive and negative control, and (iv) economically feasible. Therefore, these parameters should be optimized before proceeding with the assay of compounds in large numbers [18].

#### *3.1.1 Virtual high-throughput screening (computer simulation-guided selection)*

vHTS is an efficient approach to identify hits and lead compounds for an identified microbial target which are further optimized using medicinal chemistry approaches. The applications of vHTS can be further explored to virtually evaluate ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the identified lead chemical entities based on "Lipinski's rule." The shortlisted potential hit/lead molecules may then be evaluated *in vitro,* thus giving a meaningful rationale between computer simulations and practical experimentation. Where vHTS is a generalized term for different screening filters, it is categorized under two broad classes of virtual screening methods. These methods are (i) structure-based drug design (SBDD) and ligand-based drug design (LBDD) [19].

#### *3.1.1.1 Structure-based drug design*

Advances in HT 'omics technologies and instrumental methods of analysis such as X-ray crystallography and nuclear magnetic resonance (NMR) have solved a large number of three-dimensional (3D) structures of target proteins involved in communicable and noncommunicable diseases. These structures with specific identification numbers and resolution are available for scientific research and education purposes in protein data bank (PDB) [20]. Therefore, understanding the biologically functional interacting pocket (druggable target site) within 3D structures of the target proteins is essential to proceed with SBDD. However, if this structural information is not completely reliable or any sequence of the structural information is missing, then homology modeling is performed to generate a homologous model of the target protein [21]. SBDD is further classified under two headings; (i) docking and scoring and (ii) *de novo* drug design.

Docking and scoring is an excellent approach to predict the binding affinity and pharmacodynamic status of small chemical entities (ligands) in the active site of the target macromolecule. Scoring is an energy function which estimates the free binding energy of protein-ligand interactions such as electrostatic and van der Waals forces. Docking may be performed using two theoretical strategies namely: (i) lock and key theory, and (ii) induced-fit theory. Earlier docking programs were run using the lock and key assumptions where both the target protein and the ligand were treated as rigid structures with docking affinity dependent on the shape of the

*High-Throughput Screening for Drug Discovery toward Infectious Diseases: Options and Challenges DOI: http://dx.doi.org/10.5772/intechopen.102936*

**Figure 2.** *Classification of HTS for anti-infective drug discovery.*

interacting structures. Hence, it is termed as a rigid docking program. However, the target proteins and ligands are never in their rigid conformational state; instead, they are flexible (induced-fit docking) and undertake complementary conformational changes. Therefore, optimizing the binding pocket enables it to accommodate ligands of various shapes and sizes. This approach reduces the dropping out chances of potential false negatives [22, 23].

*De novo* design is a method of drug design that involves six different strategies: (i) identifying site point within the target site and connecting them using chemical fragments, (ii) determination of desirable fragment location, (iii) positioning fragment within the target site and linking them with linkers or scaffolds, (iv) construction of ligand sequentially within the site using fragments, (v) whole molecule conformation and interaction studies similar to docking, and (vi) random connection methods [24].

#### *3.1.1.2 Ligand-based drug design*

LBDD approach is applicable when nothing is known about the 3D structure of the target site and completely relies on the knowledge of previously established lead/drug molecules with known pharmacological/toxicological profiles and 2D/3D physicochemical descriptors. Therefore, LBDD is classified into two broad categories: (i) quantitative structure–activity relationship (QSAR) [25] and (ii) pharmacophore modeling [26]. However, scaffold hopping [27] and pseudo-receptor modeling [28] are also the strategies used in LBDD.

QSAR is a method for developing mathematical models to significantly correlate the pharmacological profile with the chemical structures within the data set using regression analysis. However, with technological advancement, the QSAR method has undergone dimensional transformations (2D and 3D). The process involves a collection of chemical data sets (in-house or external) to develop mathematical QSAR models. These models are then used to identify active compounds which are

sequentially evaluated and synthesized on various platforms, including docking, *in vitro,* and *in vivo* studies [25].

Scaffold hopping is also known as "lead hopping" as it starts with known active compounds which are modified using 1–4° chemical replacement in the known lead structure to generate a novel chemotype which is further evaluated using various platforms, including docking, *in vitro*, and *in vivo* studies [27]. In contrast, pseudoreceptor design is a method closely related to homology modeling of SBDD where presumed bioactive conformations of overlaid molecules are used to generate the target's pseudo-binding site map for further SBDD. Hence, this method is a bridge between LBDD and SBDD [28].

Pharmacophore fingerprinting is a method to identify a common "pharmacophore feature" among a set of active drug or lead molecules that may be used in SBDD and/or LBDD. The pharmacophore feature is an essential chemical portion of lead/drug molecules which is required for biological functions and may include hydrogen bond donors/acceptors, aromatic rings, hydrophilic/hydrophobic attachments, or any possible combinations. These features are enumerated in terms of three-point and four-point sets of varied pharmacophores to measure the distance in terms of bonds. Pharmacophore fingerprints thus generated are utilized for developing novel lead molecules in combination with SBDD (**Figure 3**) [26].
