COMPUTER- AIDED DRUG DISCOVERY AND DEVELOPMENT (CADDD): in silico- chemico- biological approach. Abstract. It is generally recognized that drug discovery and development are very time and resources consuming processes. There is an ever growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical arena, computer- aided or in silico design is being utilized to expedite and facilitate hit identification, hit- to- lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. Commonly used computational approaches include ligand- based drug design (pharmacophore, a 3- D spatial arrangement of chemical features essential for biological activity), structure- based drug design (drug- target docking), and quantitative structure- activity and quantitative structure- property relationships. Regulatory agencies as well as pharmaceutical industry are actively involved in development of computational tools that will improve effectiveness and efficiency of drug discovery and development process, decrease use of animals, and increase predictability. It is expected that the power of CADDD will grow as the technology continues to evolve.
Keywords: Drug discovery, Drug development, Molecular modeling, Virtual screening, Computational modeling, In silico drug design, QSAR/QSPR, Predictive toxicology. INTRODUCTIONUse of computational techniques in drug discovery and development process is rapidly gaining in popularity, implementation and appreciation. Different terms are being applied to this area, including computer- aided drug design (CADD), computational drug design, computer- aided molecular design (CAMD), computer- aided molecular modeling (CAMM), rational drug design, in silico drug design, computer- aided rational drug design. Term Computer- Aided Drug Discovery and Development (CADDD) will be employed in this overview of the area to cover the entire process.
Drug Design and Discovery Methods and Protocols. Editors: Satyanarayanajois, Seetharama D. (Ed.). · COMPUTER-AIDED DRUG DISCOVERY AND DEVELOPMENT (CADDD): in silico-chemico-biological approach. Drug design, often referred to as rational drug design or simply rational design, is the inventive process of finding new medications based on the knowledge of a.
Both computational and experimental techniques have important roles in drug discovery and development and represent complementary approaches. CADDD entails: Use of computing power to streamline drug discovery and development process.
Leverage of chemical and biological information about ligands and/or targets to identify and optimize new drugs. Design of in silico filters to eliminate compounds with undesirable properties (poor activity and/or poor Absorption, Distribution, Metabolism, Excretion and Toxicity, ADMET) and select the most promising candidates. Fast expansion in this area has been made possible by advances in software and hardware computational power and sophistication, identification of molecular targets, and an increasing database of publicly available target protein structures. CADDD is being utilized to identify hits (active drug candidates), select leads (most likely candidates for further evaluation), and optimize leads i. ADMET/PK (pharmacokinetic) properties. Virtual screening is used to discover new drug candidates from different chemical scaffolds by searching commercial, public, or private 3- dimensional chemical structure databases.
It is intended to reduce the size of chemical space and thereby allow focus on more promising candidates for lead discovery and optimization. The goal is to enrich set of molecules with desirable properties (active, drug- like, lead- like) and eliminate compounds with undesirable properties (inactive, reactive, toxic, poor ADMET/PK). In another words, in silico modeling is used to significantly minimize time and resource requirements of chemical synthesis and biological testing (Fig.
The rapid growth of virtual screening is evidenced by increase in the number of citations matching keywords “virtual screening” from 4 in 1. In his 2. 00. 3 review article, Green of Glaxo.
Smith. Kline concluded that: “The future is bright. The future is virtual” [2].
Comparison of traditional and virtual screening in terms of expected cost and time requirements. Price. Waterhouse.
Coopers Pharma 2. An Industrial Revolution in R& D report [3] stressed the reality that pharmaceutical industry needs to find means of improving efficiency and effectiveness of drug discovery and development in order to sustain itself.
This was recently echoed at the 2. Drug Discovery Technology Conference in Boston, MA by Dr. Steven Paul, head of science and technology at Eli Lilly & Co. The Price. Waterhouse. Coopers report emphasized growth and value of in silico approaches to address this issue and projected that in silico methods will become dominant from drug discovery through marketing.
It was suggested that we are in a transitional period where the roles of primary (laboratory and clinical studies) and secondary (computational) science are in process of reversal [4]. Estimates of time and cost of currently bringing a new drug to market vary, but 7–1. Furthermore, five out of 4. This represents an enormous investment in terms of time, money and human and other resources. It includes chemical synthesis, purchase, curation, and biological screening of hundreds of thousands of compounds to identify hits followed by their optimization to generate leads which requiring further synthesis. In addition, predictability of animal studies in terms of both efficacy and toxicity is frequently suboptimal.
Therefore, new approaches are needed to facilitate, expedite and streamline drug discovery and development, save time, money and resources, and as per pharma mantra “fail fast, fail early”. It is estimated that computer modeling and simulations account for ~ 1. R& D expenditure and that they will rise to 2. Role of computational models is to increase prediction based on existing knowledge [7].
Computational methods are playing increasingly larger and more important role in drug discovery and development [7–1. Fig. 2) and are believed to offer means of improved efficiency for the industry [7]. They are expected to limit and focus chemical synthesis and biological testing and thereby greatly decrease traditional resource requirements. Modern drug discovery and development process including prominent role of computational modeling. Growing presence, prominence and importance of CADDD is seen by multiple scientific sessions dedicated to it at major scientific conferences, e. Annual Meeting of SOT in San Diego 2.
Annual Meeting of AACR in DC (http: //www. Pharma. Discovery. Bethesda, MD (http: //www. User. Campaign. ID=2. Drug Design conference in London, UK (http: //www.
Gordon Research Conference- Computer- Aided Drug Design conference that dates back to 1. Quantitative Structure Activity Relationships (QSAR) (http: //www. At the 2. 00. 5 London Drug Design conference, aspiration and expectation were expressed that computational methods will achieve similar role and utility in pharmaceutical industry as already exist in automotive and airplane industries. This represents a brief overview, rather than an exhaustive review, of CADDD and the following commonly used computational approaches will be discussed: ligand- based design (e.
QSAR/QSPR) (e. g. IUPAC defines pharmacophore as: “the ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target structure and to trigger (or to block) its biological response. A pharmacophore does not represent a real molecule or a real association of functional groups, but a purely abstract concept that accounts for the common molecular interaction capacities of a group of compounds towards their target structure. The pharmacophore can be considered as the largest common denominator shared by a set of active molecules” (http: //www.
Pharmacophoric descriptors include H- bond donors, H- bond acceptors, hydrophobic, aromatic, positive ionizable groups, negative ionizable groups. They represent chemical feature complimentarity to the receptor in the 3- dimensional space. Further enhancement of a pharmacophore can be obtained by combining it with shape and exclusion volumes (steric) constraints [1.
These enhancements decrease likelihood of finding molecules with a suitable 3- dimensional arrangement of functional groups but wrong shape that could prevent them from fitting into the receptor binding site. Pharmacophore requires knowledge of active ligands and/or target receptor.
They are number of ways to build a pharmacophore.