Why the need to find new drugs in the fastest possible way has become more urgent in the current, and likely future pharmaceutical landscape. The authors also look at the challenges to research innovation in the sector, how these have impacted today’s research environment, the options for overcoming these barriers and what the real opportunities are for those companies who get it right.
Fast Drugs: A Pressing Need
The pharmaceutical industry has traditionally focused on the block-buster model wherein it has placed its efforts on a smaller number of big selling, branded drugs. While this strategy has paid off well in the past, it is no longer a fool proof method as the industry experiences an era of unprecedented change and challenges. The most successful products in pharmaceutical history are now losing patent protection and innovation pipelines have been unable to fill the gap. A report by EvaluatePharma reveals that around $290 billion worth of sales is projected to be at risk from patent expirations between 2012 and 2018. On top of this, large pharma companies are also facing threat from generic drugs which are impacting their market shares. US research firm Sanford C. Bernstein estimates that generics will reduce the revenues of the top 10 companies by between two and 40 per cent by 2015. Consequently, this will negatively impact the revenues and margins that have previously funded original, innovative research and provided returns for both patients and investors. As a result, the global pharma spend on R&D has nearly stagnated in recent years.
Forbes reported that the average drug developed by a major pharmaceutical company costs at least $4 billion, but can be as high as $12 billion. With such high costs and considering current failure rates, inventing medicines, which is the current model of drug discovery, has become somewhat of an unsustainable business practice.
This change has driven big pharma companies to take numerous measures, primarily to drive shareholder value. In addition to cost-cutting, they have downsized operations, spun off assets, restructured R&D, repurchased stock and increased dividends. These efforts, while efficient in lowering costs and returning revenues, are not by themselves a sustainable long-term solution. There needs to be an industry-wide shift in how drugs are discovered in terms of costs and the process itself. Pharma companies need to re-align their strategic focus, develop a transformation roadmap and identify the core competencies that would complement their vision. Those that regard R&D as a core element of their business will have to make fundamental alterations to the way drug discovery progresses.
Challenges to Research Innovation
Innovation is key to ensuring the longevity of the pharmaceutical industry and the healthcare community. Pharma companies must accept innovation as an underlying business strategy as the industry faces a revenue gap caused by existing products losing their patent protection and a failure to develop new products to offset this. While a relentless downward pricing pressure drives this, the biggest challenge to research innovation today concerns how research is conducted and the complexity of the information used.
Drug acceptance or rejection by pharmaceutical companies is based on genomic and proteomic data along with information coming from early discovery studies. Today, pharma companies primarily tend to use known targets for a disease as a reference and consequently attempt to create all new drugs based on these targets. Identifying new targets and pathways has always been difficult; scientists have traditionally struggled with getting results from new datasets and deriving the right intelligence to qualify molecules as potential drugs. On the other hand, a new molecule or an existing drug that is hitting a new target or pathway often carries with it a blind side in terms of potential safety issues. This is often missed during early discovery and pre-clinical stages but shows itself in phase one human trials. Not only is this a loss of resource, time and investments, it is further amplified when the intellectual property (IP) is owned by just one organization.
What Options Does Pharma Have?
The pharma industry has experimented with multiple solutions to overcome these barriers including collaborative practices, shared IP models and licensing strategies. These may, to some extent, reduce pharma companies’ operational costs and de-risk their investment, but there would still be issues around reducing time to discovery, pre-clinical and clinical trials. In the current pharma model, reducing or eliminating a discovery or development stage involves a huge increase in margins, so the industry needs to be looking to transform the way the industry innovates new drugs, which require less time and resources.
Complex problems can often be solved by harnessing existing information and data. This data could be a mix of early discovery data (e.g. HTS, in vitro data, pharmacology studies), preclinical data (e.g. ADME, toxicology information), clinical data (e.g. human trials), safety inputs being captured from the multitude of drugs in the market, electronic health and medical records (e.g. case studies, medical reports, clinical reference data from lab studies) and post marketing surveillance data which is being effectively captured as required by regulators. Although such data is readily available, the question is what intelligence can we generate out of this data, and what does this mean for future drug engineering.
Algorithms are also an option for pharma companies to derive intelligence from data. A number of algorithms could be created such as pattern matching, clustering and machine learning techniques. These methods have revolutionised other areas of research in economics, finance, forensics, and space, to name a few, and are certainly an option for Pharma companies looking to innovate their drug development and offerings.
A third option is to look at existing models which can predict, amongst others, drug-target binding, ADME profiles, and blood – brain barrier penetration capabilities. The problem in this avenue however, lies with ownership as these are built by independent groups with the data kept in isolation. If this ownership could be eliminated, or the information made open, these models could be integrated on a single platform with additional inputs such as from clinical studies, EHR-EMR data, and genomic/proteomic information from existing databases. Located on a virtual platform, this could be used for drug design and development, similar to the concept visually depicted below:
A further option that could be taken is pattern matching and clustering methods coupled with statistical and probabilistic modelling techniques. The data output could assist with new target insights for existing diseases on known or unknown pathways or identifying which patient groups should, or should not, be administered certain drugs.
Hence the applicability of ranked probabilistic modelling based on clustering and pattern matching algorithms could be multifaceted. This could impact areas such as drug design, drug safety profiling, patient stratification, and virtual drug discovery. If this could be achieved then scientists could potentially design an effective drug in real-time or derive a new target based on knowledge of existing gene-protein-disease-drug interaction networks.
Thoughts for the Future
While worldwide demand for drugs is unlikely to wane anytime soon, the question still remains: how long is drug discovery going to last? And moreover, how far are we from predicting drug-target binding patterns, safety and efficacy outcomes, or even predicting outcomes from a clinical trial without actually having one? Public confidence in a drug today is primarily driven by the data which comes out of tests done on living animals and human trials, both of which are costly resources in terms of people, infrastructure and time.
The time may be close when clinical trials and discovery give way to drug personalisation which is already emerging as a growing market trend. However, with revenues and innovation still a part of pharma, can we move solely away from today’s blockbuster driven, IP centric market to an individual specific market? And will pharma companies evolve or even be able to embrace that change? The day the industry can provide firm answers to these questions will mean a paradigm shift in the way drugs are discovered.
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