Increasing costs of drug development and reduced pipeline productivity have been growing concerns for new drug development in recent years. A number of potential reasons for this outcome have been considered. One of them is a general perception that applied sciences have not kept pace with the advances of basic sciences. Therefore, there is a need for the use of alternative tools to get answers on efficacy and safety faster, with more certainty and at Lower cost .One such alternative tool is the in silico drug design or the computer aided drug design (CADD). In silico drug design can play a significant role in all stages of drug development from the preclinical discovery stage to late stage clinical development. Its use in drug development helps in selecting only a potent lead molecule and may thus prevent the late stage clinical failures; thereby a significant reduction in cost can be achieved. This article gives an insight to all the aspects of in silico drug design; its potential, drivers and restraints, current scenario and the future prospects.
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Key words- in silico drug design, Computer aided drug design, Virtual screening
Drug discovery and development is a complex, lengthy process and failure of a candidate molecule can occur as a result of combination of reasons such as poor pharmacokinetics, lack of efficacy, Side effect and commercial reasons .Most drugs are discovered by either modifying the structure of known drugs, by screening compound libraries or by developing proteins as therapeutic agents. With the advent of genomics, proteomics, bioinformatics and technologies like crystallography, NMR, the structures of more and more protein targets are becoming available. So there is a need for computational tools that can identify and analyze active sites and suggest potential drug molecule that can bind to these sites. In silico models fill this research lacuna. Studies right from molecular docking, molecular dynamics, quantum mechanics, QSAR to ADMET prediction including dissolution studies are performed in silico. Availability of huge database of drugs from drug bank, protein data bank coupled with recent advances in technology further fuel the use of in silico models.
In the preceding sections various aspects of in silico drug design will be discussed upon beginning with an insight to the conventional drug discovery process and its pitfalls, the need for an alternative tool to reduce the R&D time cycle as well as the cost involved and how in silico drug design could play the role of being one such alternative tool. Later the discussion focuses on a list of various globally available in silico models emphasizing their possible intervention at various stages of drug design, drivers and restraints in implementing these models, current status of in silico drug design and Future prospects
Drug discovery process
The process by which a new drug is brought to market stage is referred to by a number of names most commonly as the development chain or "pipeline" and consists of a number of distinct stages. Broadly it can be grouped under two stages Preclinical and the Clinical. Preclinical involves a two-step process. The first step is to identify and model the biological target within the body (the protein). The second step involves identifying a lead compound (molecule) that exhibits drug-like properties with respect to this protein followed by preliminary screening in animals. Subsequently, the drug goes through many phases of clinical development in humans. In the clinical phase, the drug is administered to human volunteers to determine:
â€¢ The passage of the drug through the body-from the time it is taken to the time it is excreted
â€¢ The effect of the drug on the body
â€¢ Its effectiveness on the disease being targeted
â€¢ Undesirable side effects of the drug
The description of the process drug discovery process is set out in the box below
Cost and the time involved in the drug discovery process
PhRMA(Pharmaceutical manufacturers of America) estimates the cost at US$500 million over a period of 11 years from the initial research stage to the successful marketing of a new drug1. More recent estimates by DiMasi at the Tufts Center for Study of Drug Development (CSDD) that was published in 2003 put the average cost at US$802 million spread over 12 years2, while the Boston Consulting Group estimates the cost as $880 million over 15 years3. At present the cost involved in the drug discovery process may range from $ 800 million to $1.8 billion. These estimates are averages and there is significant variation in both time and cost, depending on the nature of the disease being targeted, the type of drug being developed and the nature and scope of the clinical trials required to gain regulatory approval.
Pitfall in current drug discovery process-The productivity gap
Always on Time
Marked to Standard
A recent US Government Accountability Office (GAO) report4 found that Pharma R&D spending grew by 147% between 1993 and 2004 while the overall number of NDAs submitted to the FDA increased only 38% and, worse still, the number of NDAs submitted for the presumably more innovative NMEs increased by only 7% in that time.
The attrition rate is unacceptably high. Only 1 out of 12 drugs entering clinical trials become a new drug5. A particular worry for the pharmaceutical industry is that, despite a variety of approaches being used for R&D, attrition rates remain high during drug development. There are a number of factors attributed to the high attrition rates observed, but the number of active substances with poor pharmacological properties has been cited as a major concern. These are active substances that lack appropriate bioavailability, exhibit poor pharmacokinetics or cause adverse events and will therefore need to be withdrawn from development. It is estimated that these types of failures represent approximately 50% of all failures in drug development.
The sequencing of the human genome in 2000 raised widespread hope for a new era in the prevention and treatment of disease created by the ongoing investment in biomedical research but that new era has not yet arrived. Instead, 2000 marked the start of a slowdown in new drug and biologic submissions to regulatory agencies worldwide. The submission of innovative medical device applications has also slowed recently. This means fewer new products can be approved and made available to patients. At a time when basic biomedical knowledge is increasing exponentially, the gap between bench discovery and bedside application appears to be expanding. This declining productivity is partly due to the fact that all the simple disease targets have been addressed and those that are left are much more difficult to address from a traditional chemistry perspective, or their role in disease is not well understood.
Need for an alternative tool
From the above facts and figures it is evident that there is an urge for an alternative tool that would not only shorten the R&D time cycle but also reduce the ever increasing cost involved in the drug discovery process. There is a general perception that applied sciences have not kept pace with the advances of basic sciences. Modeling and simulation could play a key role in alleviating the industry malaise outlined in an FDA report released last year, Innovation and Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products. It noted that while spending on biomedical research has increased greatly over the last decade, the submission of new molecular entities has remained flat .The report also pointed out that a drug entering phase I trials in 2000 was no more likely to reach the market than one entering phase I trials 15years earlier.
Outdated technologies may be one reason for those discouraging numbers, the report states: "Often, developers are forced to rely on the tools of the last century to evaluate this century's advances." But the agency believes there are steps industry can take. "As biomedical knowledge increases and bioinformatics capability likewise grows," the report states, "there is hope that greater predictive power may be obtained from in silico (computer modeling) analyses such as predictive modeling." The report, citing data from PricewaterhouseCoopers, states that "extensive use of in silico technologies could reduce the overall cost of drug development by as much as 50%".
Impact of technology
The process of finding a drug molecule that attaches itself to the target protein in the body has now moved from the lab to the computer. The words in silico drug design and computer aided drug design are almost synonymous. In the post genomic era, computer-aided drug design (CADD) has considerably extended its range of applications, spanning almost all stages in the drug discovery pipeline, from target identification to lead discovery, from lead optimization to preclinical or clinical trials.
In silico drug discovery process comprises of 3 stages
Stage 1-It involves Identification of a therapeutic target and building a heterogeneous small molecule library to be tested against it. This is followed by the development of a virtual screening protocol initialized by either docking of small molecules from the library or building these structures in the active site by employing De novo design methods.
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Stage 2- These selected hits are checked for specificity by docking at binding sites of other known drug targets.
Stage 3-These selected hits are subjected to detailed in silico ADMET profiling studies and those molecules that pass these studies are termed as leads.
In silico ADMET prediction
There is considerable interest in computational models to predict drug safety in drug discovery and development. Significant adverse toxicological findings for a drug in late-stage clinical trials or post-marketing can cause enormous financial losses and place patients at risk. The earlier such molecules are identified and the drug development process halted the better. In addition, insights into the toxicological potential of a scaffold or series of structures early on in the drug discovery process could help medicinal chemists to prioritize particular scaffolds or hits. Finally, computational toxicity models could be used to help understand pre-clinical toxicity data and select appropriate experimental end-points for further studies during clinical candidate selection and early clinical studies. There are tools to predict toxicities like
CYP450 inhibition and
In silico prediction of drug-drug interactions
Recently, metabolic drug-drug interactions (M-DDI) have raised some high-profile problems in drug development resulting in restricted use, withdrawal or non approval by regulatory agencies. The use of in vitro technologies to evaluate the potential for M-DDI has become routine in the drug development process. Nevertheless, in the absence of an integrated approach, their interpretation and value remains the subject of debate, and the vital distinction between a useful ''simulation'' and a precise ''prediction'' is not often appreciated. Various in silico software are now available for the simulation of M-DDI.One such software is SIMCYP.
Virtual screening involves the docking of selected lead molecules against the biological target. This is followed by a scoring pattern. There are a number of software available for this. Some are commercially available and some are free to use.
Commonly used Programs
TOPKAT, Tsar, LigandGel, ZDOCKPro, DS
MedChem Explorer, Cerius2, AEI
ACD/LogD Suite and ACD/Log Sol Suite,
ACD/LogD Batch and ACD/Log Sol Batch,
ACD/Structure Design Suite, ACD/PhysChem batch
ADMET Modeler, ADMET Predictor, Class
Pharmer 4.0, GastroPlus, DDDPlus
Simulations Plus, Inc.
ToxML, LeadScope Toxicity Database, LeadScope
Known Drugs Databases, LeadScope Enterprise,
Algorithm Builder, QSAR Builder, ADME Boxes
v. 3.0, Tox Boxes v. 1.0, ADME/Tox WEB,
DMSO Solubility, ADME Batches, Absolv
Improvement in drug attrition rates drive increased adoption of in silico technologies
Costly failures of late drug development spurs the use of in silico models for early ADME/Tox screening
Improved computational power drives the development of in silico ADME/Tox screening products
Improved and reliable models increases adoption by pharmaceutical companies
Increased rate of target identification drives the adoption of in silico models that ultimately seek to screen targets at the same rate as they are discovered.
Collaborations/partnerships between in silico product vendors and pharmaceutical companies has resulted in the ability to develop "global" off-the-shelf products as well as "local" customized products
Lack of accurate/reliable experimental data restricts the development of improved in silico ADME/Tox models
Predictive value of many in silico ADME/Tox technologies remains unproven
The risk that a potentially safe and viable drug candidate may fail by utilizing in silico models and subsequently not put forward for in vitro/in vivo analysis
No complete list of successful projects regularly updated in which modeling & simulation had an important effect
Lack of test standardization and Proof of concept remains a major constraint
In silico modeling will play a role in the future of pharmaceutical discovery and development, but the extent of that role remains to be seen. "At this point [it won't] fizzle out," says Mallalieu, senior principal scientist in discovery pharmacology at the Nutley "but I wish it spread faster than it has, and I think the reason that it hasn't is that it hasn't caught on. It's a vicious cycle. You have to prove yourself to grow, but you need a certain critical mass in order to prove yourself."
Pfizer's Lalonde is optimistic. "The ones that can successfully implement this will probably be swallowing up other companies that are not so successful, because they will keep doing it the old-fashioned way and driving up the cost to astronomical levels, costs that will be very hard to justify in the marketplace. All successful companies will have to do this routinely because it's just too expensive to do it by trial and error, the way it's often been done in the past."
In the selection of new drug candidates, many efforts are focused on the early elimination of compounds that might cause several side effects or interact with other drugs. In silico techniques help in this regard and they are going to become a central issue in any rigid drug discovery process. In silico technology alone cannot guarantee the identification of new safe and effective lead compound but more realistically future success depend on the proper integration of new promising technologies with the experience and strategies of classical medicinal chemistry.