How To Save Costs Using Structure Based Drug Design Methods
Structure based drug design methods have long been the darling of large pharmaceutical companies, and scientists readily understand why. Using structure based drug design paradigms like QSAR or others, drug developers can quickly iterate on recently off-patent drugs to create newly branded drugs of their own, saving an immense amount of money in preclinical and also early clinical testing stages.1 The fundamental idea is that by using computer assisted design, publically viewable drug structures that have been proven to be effective and safe have their structure modified slightly, and then the resulting drug candidate is tested in vitro to see if, broadly speaking, it behaves the same as the structure that they’re derived from. Perform the basic pattern of modification and testing enough times, and most pharmaceutical companies figure that they’ll find another winning drug that they can put under new intellectual property and right back on the market to retain the competitive advantage lost by their prior drug reaching the end of its patent’s life. Recently, drug manufacturers have started to perform much of their structure based drug design in silico before moving to the in vitro preclinical stage, pointing to a future of software-based recycling of old drug designs–a move that’s sure to save even more money than prior practices.2 3
Why Look For Cost Savings In Structure Based Design?
As recently as 2010, the drug discovery community was alight with worry: The costs of new drug discovery were flying through the roof in the wake of new biotechnologies, which allowed production of novel drugs, though at a much higher price than before.4 Forward looking drug design had long been the dominant paradigm, meaning that chemicals were first generated, tested for useful biological activity, then deployed to trials.5 Needless to say, forward pharmacology was quite wasteful in terms of the ratio of effective molecules created to number of drugs put on the market.
As basic biological information regarding receptor chemistry and genetic targets of drugs became more advanced, the potential for reverse pharmacology was born.6 Now, researchers could tailor a molecule to a particular biological target. This was a game changer and effectively enabled structure based design to become a cost saving measure. Once a drug was found effective for a given physiological target, researchers could bet that minor modifications to that drug’s structure would retain at least some of the desired biopharmaceutical properties of the old drug.
Which Structure Based Design Methods Can Save The Most Money?
All structure based drug design methodologies have a huge potential to cut costs, but the question that keeps pharmaceutical discovery executives up at night is which method can cut costs the most.
There are a few main options, and at present it’s hard to weigh in definitively on which method is the best at saving money the two strongest candidates for cost cutting are, at the moment, molecular modeling–called Molecular Docking And Molecular Modeling in the industry lingo– and QSAR.7 8 Molecular Docking And Molecular Modeling involves:
- Identification of a molecular target whose ligation has a physiological effect that could be beneficially manipulated
- Identification of a molecular ligand for that molecular target from known and characterized ligands
- Identification of structural modifications of the ligand which retain its biological activity
- Identification of modifications of the ligand in question that are eligible for patenting
- Starting experimentation based off which ligand variants aren’t yet patented
In contrast, QSAR is a mathematical approach which models known drug structures and compares their confirmed interaction with the desired pharmacological target–think about it as looking at a fruit from a massive pile of fruits and determining whether it’s an apple or whether it’s an orange, and discarding all the apples. After discarding the irrelevant drug structures that have no chance of producing a physiological change with the desired drug target, QSAR randomly seeds changes to the structure of the remaining drug candidates that are confirmed to have pharmacological action at the target site. At this point, QSAR delivers a list of probably active drug structures, and ranks them by their affinity for the target site. From this list, researchers can choose where to start in silico or in vitro screening experiments.
A Quick Cost Savings Comparison
Given that both of these methodologies have had a number of years to mature and become effective, some drug discovery groups use both to find potential therapeutic candidates, but there are certain contexts in which it makes sense for a particular method to be used over the other for cost savings. The cost savings relate to the scale, hardware, and expertise of the organization in question.
For a smaller biotech company with a specialization in biologics, using molecular modeling techniques is far more likely to render a fruitful lead than a method like QSAR. Smaller groups don’t have the massive chemical synthesis infrastructure required to create hundreds of variants of a single molecular structure like QSAR would necessitate. Building the capability to do so would be extremely expensive, and likely require raising additional funding. In contrast, molecular modeling based drug design methods provide discrete and actionable pieces of information along the lines of “this receptor uses this ligand, and this alternative version of that ligand is also bioactive.” Smaller drug developers can easily make use of this information to focus their efforts onto a single point.
For a major pharmaceutical company, this kind of information is small potatoes, however. So what if there’s one modification of one ligand which might be worth testing– that amount of information leaves most of the pharmaceutical company’s drug development prowess ground to a standstill for lack of task. Thus, while they’re likely to use molecular modeling software for precision interventions designed for intellectual property sniping based around what competitors are doing, methods like SAR provide a much larger funnel for their drug development resources to chew on.
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- “Advances in computational methods to predict the biological activity of compounds.” July 2010, https://www.ncbi.nlm.nih.gov/pubmed/22823204. ↩
- “Drug repositioning by structure-based virtual screening.” January 2013, http://pubs.rsc.org/en/content/articlelanding/2013/cs/c2cs35357a/unauth#!divAbstract. ↩
- “The holistic integration of virtual screening in drug discovery.” April 2013, http://www.sciencedirect.com/science/article/pii/S135964461300024X. ↩
- “How to improve R&D productivity: the pharmaceutical industry’s grand challenge.” March 2010, https://www.nature.com/nrd/journal/v9/n3/full/nrd3078.html. ↩
- “Classical vs reverse pharmacology in drug discovery.” September 2001, http://onlinelibrary.wiley.com/doi/10.1111/j.1464-410X.2001.00112.x/abstract. ↩
- “Identifying Druggable Disease-Modifying Gene Products.” September 2009, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2787993/. ↩
- “Molecular Docking and Structure-Based Drug Design Strategies.” 2015, http://www.mdpi.com/1420-3049/20/7/13384/htm. ↩
- “Multiscale quantum chemical approaches to QSAR modeling and drug design.” December 2014, http://www.sciencedirect.com/science/article/pii/S1359644614003900. ↩