1. Introduction

The Nobel Prize in Physiology or Medicine 1945 was awarded jointly to Ernst Boris Chain, Sir Howard Walter Florey and Sir Alexander Fleming for the mass production of penicillin discovered by the latter almost two decades before. At that time, the average life expectancy at birth in Mexico was about 45 years.¹ Currently, life expectancy for newborns is around 75 years. This increase can be explained by many factors, including the significant amount of medication that physicians prescribe today to extend the life of a patient.

Most of the effects of medicines are based on the interaction between therapeutic chemical compounds (drugs) and proteins (targets), such as G-protein-coupled receptors, ion channels, proteases, kinases or nuclear hormone receptors, among others. Therefore, we might wonder how many new drugs are still to be discovered by researchers. The answer would be almost an ‘unlimited’ quantity. The number of potential targets remains unclear. However, recent estimates claim that the number of current targets is over an order of magnitude lower than it could be.^2,3 On the other hand, the number of organic molecules that can act as drugs is also a matter of debate. More than 100 million small chemical compounds have been already synthesized for their screening on specific targets in public and private laboratories.⁴ In addition, it is not only a matter of quantity but also of quality. Until recently, medicines have been discovered either as a result of unexpected investigations—by inspection of natural substances traditionally considered as therapeutic—or in extensive experimental blind screening studies.⁵ These drugs, although proven to be useful in the treatment of several pathologies, have significant downsides, like important side effects or low efficiency. As they were discovered, not designed, new drug generations have to overcome these difficulties.

The process of discovery and development of novel drugs is known to be time-consuming and expensive. On average, the standard process of discovery and development of drugs to marketing takes from 10 to 15 years. Furthermore, the average cost for research and development of each effective drug is estimated at $1.8 billion USD.⁶ In this context, it is not surprising that the development of new strategies over the last decades has emerged to make the processes much more rational and efficient using new supercomputers combined with the knowledge and experience of researchers. To this day, physicians, biologists, chemists, physicists, and computer scientists work, hand in hand, with the goal of offering to patients better and more selective drugs to improve their quality of life. Nevertheless, there is still a long way to go in the field of drug discovery, or should we say, drug design?

In this article, we first introduce the concept of computer-aided drug discovery and design with a summary of the leading computational techniques that include drug-repositioning approaches and lead optimization techniques. In the second section, we describe how these approaches have been successfully applied. In addition, we review the concept of rational design and present some of the most representative examples of molecules identified by its means. Key principles are illustrated through case studies exploring the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

2. Computer-aided drug discovery and design

Since the advent of the X-ray diffraction to unveil the chemical composition and three-dimensional (3D) geometry of a small organic molecule in 1932,⁷ a large number of proteins have been solved either by X-ray or by nuclear magnetic resonance (NMR) spectroscopy and are available at open access protein databases (http://www.rcsb.org). This information allows researchers to understand and characterize many physiological processes based on interactions between proteins or between proteins and small molecules (ligands), as the case of the drug-target binding.

In 1962, Max Perutz and John Kendrew were awarded the Nobel Prize in Chemistry for the first solved high-resolution structure of protein (myoglobin). Since then, several other studies in crystallography determination of protein structure have been awarded the Nobel Prize⁸ until the recent Nobel Prize in Chemistry 2012, which was awarded jointly to Brian Kobilka and Robert Lefkowitz for the structural and functional studies on G-protein-coupled receptors (GPCRs).

With the chemical composition and 3D relative position of each atom in a target, the quest to find hit molecules that could potentially act as drugs has evolved considerably: from a blind screening process that hopes for finding molecular hits essentially by serendipity to an approach often called ‘rational’ drug discovery and design⁵. In the 80's, this was the case of the first angiotensin-converting enzyme (ACE) inhibitor Capoten (captopril), the first drug optimized using structural information. In 1997, nelfinavir mesylate (Viracept)—an HIV protease inhibitor—was the first drug with a design completely driven by the structure of the target approved for the US market.⁹ These discoveries were only the beginning of a frantic career in search of novel, faster, and cheaper methodologies, and computational algorithms and techniques to develop and design new drugs. Moreover, to sample more compounds over the target (screening process) in less time and to acquire a priori key knowledge and expertise to design the library of chemical compounds for further screening in a more precise manner.

After solving protein structures at high resolution, the relevant revolution came when computational models based on simple physical laws were able to mimic the interactions between the organic molecules, atom by atom. Apart from the 3D structure of a molecule, the electrostatic charges and dipoles, the atoms’ van der Waals radii, the parameters of covalent bonds, torsions, and dihedral angles were considered. Researchers today can approach real systems by using virtual or in silico experiments with the support of computational facilities, such as powerful workstations or supercomputers. This advancement has been the keystone, which has paved the way for a more rational approach to the query of efficient, selective and fewer side effects drugs, and at the same time making the process cheaper and less time-consuming.

At present, with these technologies, screening more compounds in less time at a lower cost is possible (virtual screening). The Computer-Aided Drug Discovery and Design (CADDD) era, where computer simulations of chemical systems have triggered the possibilities in this field, has allowed researchers to make in silico improvements. Among these advances, there are the resolution of 3D structures using computer models, the optimization and design of new compounds, and the understanding and characterization of the atomic mechanisms of previous drugs or natural substances. Furthermore, breaking the paradigm of orthosteric drugs (drugs binding to the target at the specific active site) to expand the search of therapeutic molecules to allosteric modulators and bitopic drugs.

The impact of these methodologies, as well as the science development in understanding and modeling the all-atom chemical and biochemical processes and reactions from a computational and physical perspective, has recently been recognized with the Nobel Prize in Chemistry (2013) to the physicists Martin Karplus and Michael Levitt together with the chemist Arieh Warshel.

Currently, many technologies have been developed to boost the efficiency of the drug discovery process. Since its emergence, CADDD has experienced a rapid increase in development, to which many different research groups around the world have made significant contributions. It has also emerged to harness various sources of information to facilitate the development of new drugs that modulate the behavior of therapeutically interesting protein targets, accelerating the early-stage pharmaceutical research. Rapid developments in CADDD technologies have provided an environment to expedite the drug discovery process by enabling huge libraries of compounds to be screened and synthesized in short time and at a very low cost. Today, CADDD is a widely used term to represent computational tools and sources for the storage, management, analysis and modeling of compounds¹⁰ used at almost every stage of a drug-discovery project, from lead discovery, optimization, target identification and validation, to even preclinical trials.¹¹

3. Ligand- and structure-based methods

Evidence of computational drug design success in the field of drug development is reflected in a significant number of new drug entities that are currently in clinical evaluation.

Computational drug design has emerged to harness different sources of information to facilitate the development of new drugs that modulate the behavior of therapeutically interesting protein targets. These computational approaches are classified mainly into two families: ligand- and structure-based methods.

Ligand-based methods use the existing knowledge of active compounds against the target to predict new chemical entities that present similar behavior.¹² Given a single known active molecule, a library of molecules may be used to derive a pharmacophore model to define the minimum necessary structural characteristics a molecule must possess in order to bind to the target of interest. Comparison of the active molecule against the library is often performed via fingerprint-based similarity searching, where the molecules are represented as bit strings, indicating the presence/absence of predefined structural descriptors.¹³

In contrast, structure-based methods rely on targeting structural information to determine whether a new compound is likely to bind and interact with a receptor. One of the advantages of the structure-based drug design method is that no prior knowledge of active ligands is required.¹⁴ From a drug 3D structure it is possible to design new ligands that can elicit a therapeutic effect. Therefore, structure-based approaches contribute to the development of new drugs through the discovery and optimization of the initial lead compound.

Currently, the combination of ligand- and structure-based methods has become a common approach in virtual screening since it has been hypothesized that their integration can enhance the strengths and reduce the drawbacks of each method.

In this section, some of the most representative computational approaches used to design, optimize and develop a new drug are described. Although there are remarkable differences among them, they share a common goal: harvesting potential ligands or hits with the capability to bind to the target from an extensive database of generic small chemical compounds (Figure 1). To achieve this goal, many essential steps and decisions have to be made in order to eliminate from irrelevant compounds at the beginning, to end up with those that show better potential activity or have side effects and show interaction with other drugs. This process performed with the assistance of computational algorithms is called virtual screening.

Figure 1 Workflow for hit identification: from data preparation to finding new leads. (A) Standard in silico drug design cycle consists of docking, scoring and ranking initial hits based on their steric and electrostatic interactions with the target site, which is commonly referred to as virtual screening. Generally, in the absence of structural information of a receptor, and when one or more bioactive compounds are available, ligand-based virtual prescreening is utilized. This prescreening method is carried out by similarity search. The basic principle behind similarity searching is to screen databases for similar compounds with the backbone of the lead molecule. (B) In many situations, 2D similarity searches of databases are performed using chemical information from the first generation hits. (C) One alternative approach employs a ligand-based pharmacophore strategy that is often partnered with structure-based docking that uses a more stringent scoring matrix to determine the relative score made by matching two characters in a sequence alignment. This enhances the enrichment of initial hits and identifies the best compounds for computational evaluation, which are the second generation hits. (D) In the second phase, the molecular interactions between the target and the hits often identify ligand-based sites for optimizing these metrics for a unique molecular chemotype. (E) Computer algorithms, compounds or fragments of compounds from a database are positioned into a selected region of the structure (docking). These compounds are scored and ranked based on their steric and electrostatic interactions with the target site. (F) Structure determination of the target in complex with a promising lead from the first cycle reveals sites on the compound that can be optimized to increase potency. 

4. Integrated methods

Recently, there has been a trend towards integrating both structure- and ligand-based methods, which use information on the structure of the protein or the biological and physicochemical properties of bound ligands, respectively. The aim is to enhance the reliability of computer-aided drug design approaches by combining relevant information from the ligand and the protein. At the simplest, building a 3D pharmacophore to find potential ligands and performing further docking studies on the target constitutes a combined approach. These integrated approaches fall into two classes: interaction-based and docking similarity-based methods. Interaction-based methods focus on identifying the key interactions between the protein and ligand using available physicochemical data. These interactions are then used to screen small molecule libraries for compounds capable of producing such an interaction profile. In contrast, docking similarity-based methods merge structure-based docking methods with ligand similarity methods. With these combinations, virtual screening becomes very efficient and allows exploring libraries of up to 10⁶ small molecules.^{37,38,39,40,41}

5. Virtual screening

Before having the computational resources and techniques to perform a computational or rational drug design, researchers had to repeat an exasperating number of trial-and-error procedures against the targeted protein in their laboratories to test some hundreds of compounds available in chemical libraries. Many times, this challenge was only affordable for big pharmaceutical companies and institutions, being a matter of luck, expertise or intuition for the rest of laboratories. The number of possible chemical compounds in an exhaustive search should be around 10⁶. Moreover, this amount of small molecules can only be tested and filtered through in silico or virtual screening (VS) with the use of powerful computers. The process of virtual screening consists of selecting compounds from large databases by using computational tools rather than physically screening them. Through this process, active compounds that could modulate a particular biomolecular pathway can be rapidly identified. The relevance of this technique is that the cost/benefit ratio justifies the presence of this approach in almost any drug design and development project, even though the number of different algorithms and strategies for successful virtual screening is a matter of constant debate and depends on the nature of the project.

Virtual screening offers many advantages over physical screening. It is significantly less resource-intensive and faster. In addition, even compounds that are not yet available can be first evaluated by virtual screening, and if they are found promising, they can be bought or synthesized. Thus, millions of compounds can be analyzed by virtual screening. However, it is important to keep in mind that virtual screening is still a relatively coarse filter, which particularly considers structure-based screening because the prediction of binding affinities remains one of the holy grails of computational chemistry.⁴² Nevertheless, several successful examples have been published and recently reviewed.⁴³

6. Successful applications of computational drug discovery and design

Rapid developments in CADDD technologies and methodologies have provided an environment to expedite the drug discovery process by enabling huge libraries of compounds to be screened and synthesized in short time and at a low cost. In the last years, numerous projects in the search for new or optimized drugs have successfully applied these approaches. To illustrate the relevance of the applications, some of them regarding in silico target prediction, hits identification or leads optimization are reviewed.

7. Successful applications in cancer drug discovery

The development of new anticancer drugs proves to be a very elaborate, costly and time-consuming process. CADDD is becoming increasingly important, given the advantage that much less investment in technology, resources, and time are required. Due to the dramatic increase of information available on genomics, small molecules, and protein structures, computational tools are now being integrated at almost every stage of the drug discovery and development. Given the 3D structure of a target molecule, chemical compounds may have a potentially higher affinity for their target when are designed rationally with the aid of computational methods. In recent years, several cases of successful applications of structure-based drug design have been reported.

An interesting example of structure-based pharmacophore modeling is the identification of p53 upregulated modulator of apoptosis (PUMA) inhibitors.⁵² PUMA is a pro-apoptotic protein, member of the Bcl-2 protein family. Its expression is regulated by the tumor suppressor p53.^53,54 PUMA ablation or inhibition leads to apoptosis deficiency underlying increased risks for cancer development and therapeutic resistance. This cancer-treatment target is central in mitochondria-mediated cell death by interacting with all known antiapoptotic Bcl-2 family members.⁵⁴ Based on the binding of BH3-only proteins with Bcl-2-like proteins, some approaches have been used to identify small molecules that can modulate these interactions and therefore, inhibit apoptosis. Most of the efforts have focused on the development of Bcl-2 family inhibitors that mimic the actions of the pro-apoptotic BH3 domains. Such compounds have been identified through computational modeling, structure-based design, and high-throughput screening of natural product and synthetic libraries.⁵⁵

On the other hand, Liu et al. reported a combinatorial computational strategy for discovering potential inhibitors against insulin-like growth factor-1 receptor (IGF-1R), which is associated with several cancers, including breast, prostate, and lung cancer. IGF-1R belongs to the tyrosine kinase family and plays a pivotal role in the signaling pathway involving cell growth, proliferation, and apoptosis. The initial hit obtained from hierarchical VS was subsequently used as the query scaffold for the substructure search to build a focused library. The library was then screened against IGF-1R with an in-house pharmacophore-constrained docking protocol. Eventually, 15 out of 39 compounds exhibited inhibitory activity in enzymatic assessment. Strikingly, the two most potent inhibitors demonstrated an excellent inhibitory potency (IC50 = 57 and 61 nM, respectively), and also presented significant selectivity over the insulin receptor (IR), which is highly homologous to IGF-1R.⁵⁶ The authors concluded that the promising selective IGF-1R inhibitors—aside from being potential antitumor agents—could be investigated as molecular probes to differentiate the biological functions of IGF-1R and IR.

Another successful example of small molecules designed using a ligand-based approach is the case of tubulin inhibitors.⁵⁷ Tubulin polymerization, an essential component of cell cycle progression and cell division represents an important target for anticancer therapy. Several antimitotic agents (paclitaxel, colchicine, and the vinca alkaloids) have been discovered and are clinically used, but they often show significant toxicity, low bioavailability, rapidly acquired resistance and the resulting overexpression of drug-resistant pumps that eject these antimitotic inhibitors from the cell. However, due to these unfavorable properties, researchers have devoted substantial effort to discover new agents with more tolerable and efficient properties, particularly as it is believed that antimitotic agents could work to diminish blood supply to cancerous tumors.

Liou et al. based their model generation on a set of 21 indole-derivatives synthesized originally for potential tubulin inhibition and used structure-activity relationship (SAR) analysis to drive it. These compounds were chosen such that their inhibitory IC50 values spanned over three orders of magnitude, from 1.2 nM to 6 μM. Based on the chemical similarities of these compounds, the authors selected four common pharmacophoric features—including a hydrogen bond donor, a hydrogen bond acceptor, a hydrophobic group, and a hydrophobic aromatic group—for the construction of a chemical library. Subsequently, 142 compounds were biologically tested using a human oral squamous carcinoma cell line (KB). Among these 142 biologically tested compounds, four were found to inhibit the KB cell line with IC50 values of 187 nM, 2.0 μM, 3.0 μM, and 5.7 μM, respectively. The most potent compound of these four active molecules was also found to inhibit the proliferation of other cancer cell lines like MCF-7 (breast cancer), NCI-H460 (human non-small-cell lung cancer), and SF-268 (human central nervous system cancer), with IC50 values of 236 nM, 285 nM, and 319 nM, respectively.⁵⁸

Another example of small molecules designed using a computational approach is the case of an I-Kappa-B Kinase β (IKK-β) inhibitor.⁵⁹ IKK-β, which is a key player in the NF-κB signaling pathway, represents yet another potential target for the treatment of cancer in addition to inflammation. In 2011, Noha et al. decided to use ligand-based pharmacophore modeling to identify new compounds with affinity to IKK-β. The ligand-based pharmacophore model for this study was based on a set of five compounds with high activity (IC50 values of 100 nM or less) and at least a several-fold difference in selectivity for IKK-β over NFκB in an attempt to develop an IKK-β inhibitor-specific pharmacophore model. The model was further refined using a dataset extracted from the literature of 44 biologically inactive compounds, 128 active compounds, and 12,775 several random decoy compounds. The top ten high-scoring compounds were tested in vitro. Among these, the most potent inhibitor (compound NSC-719177) could inhibit IKK-β with an IC50 value of approximately 6.95 μM.⁵⁹ Cell-based analyses were also conducted to test the ability of compound NSC-719177 to inhibit NFκB activation in HEK293 cells stably transfected and carrying a luciferase reporter gene activated by a promoter composed of multiple copies of the NF-κB response element. Compound NSC-719177 was found to have a cell-based assay IC50 value of approximately 5.85 μM and exhibited dose-dependent activity in inhibiting TNF-α-induced luciferase activity. Therefore, Noha et al. were able to demonstrate the successful application of ligand-based approaches to the identification of low micromolar inhibitors against IKK-β.⁵⁹

In an attempt to articulate how the iterative process of structure-based design can lead to the development of drugs with pharmacological potential, more examples from the literature are listed in Table 1.^{60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100}

With the evolution of significantly more sophisticated molecular modeling tools and the use of high-throughput X-ray crystallography for a target alone or in complex with small molecules, rational drug design techniques have become an indispensable instrument for the development of target-based therapies.

Considering the relevance of the examples cited above, and the importance of these approaches, our research group is interested in finding some molecules capable of inhibiting the transcription factor called Yin-Yang 1 (YY1). This factor has been observed to be overexpressed in cancer patients, and its activation is significantly involved in chemotherapy resistance mechanisms. In pediatric patients with acute lymphoblastic leukemia (ALL) it was reported that YY1 is overexpressed.¹⁰¹ The in vitro inhibition in ALL cell lines showed that YY1 has an important implication in chemoresistance in these cells. Based on these findings, our research group was particularly interested in identifying some inhibitors of the activity of this transcription factor employing CADDD tools. The elucidation of the structure of the YY1 and site-binding domain provides a new framework to understand the functions of this transcription factor and leads to the development of rational drug design for the treatment of ALL. An overview of YY1 structure and activity, its actions in lymphoblastic leukemia and chemoresistance, and how structural information and high-throughput screening have been or can be used for drug discovery will be provided in the future.

The process of novel drug discovery and development is recognized to be very expensive and time-consuming. However, thanks to recent advances in the development of physical and chemical models to simulate biomolecular processes, together with the production of increasingly powerful computational resources, discovering and designing new drugs as anticancer drugs is an affordable task for many research institutions and laboratories today. With the required computational hardware and software, and the expertise in biochemistry, biophysics, and biology, many projects that previously demanded a significant investment in time and money can be done today by a small group of researchers in their workstations. Moreover, challenging projects not even conceivable two decades ago can be today tackled with the access to a supercomputer. By using CADDD approaches, researchers are not only accelerating their steps and projects. Implementing molecular simulations in biomolecular research projects has increased our knowledge in fields such as structural and chemical biology at the point where these tools are considered as other useful facilities in the laboratory. The optimization of these techniques and methods occurs this way naturally in its theoretical feedback signaling system. Computational models generate useful predictions to be checked with experimental results, and biologists and physicians demand approaches that are more accurate to computational scientists.