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The pharmaceutical industry was forced to undertake significant structural changes to address increasing challenges from regulatory policies, patent expiration of blockbusters, lower productivity, innovation gaps and price competition from generics during the last two decades.

Outsourcing of research, layoffs, mergers and acquisitions became a common place in an industry which had previously been almost resistant to these activities. In the timeframe of 2000-2010, more than 1300 mergers and acquisitions with a value of more than $690 billion took place globally.

More than 300.000 pharmaceutical jobs were cut down because of these activities. This significant decrease in internal resources forced the industry to investigate several strategies to improve efficiency of R&D.

Many companies began to increase their emphasis on external driven R&D activities. Today, the industry is changing their business model and changing their position that all R&D activities should be done internally.

Contract Research

Contract Research Organizations (CROs) and Contract Manufacturing Organizations (CMOs) became an integral part of the pharmaceutical industry, providing active pharmaceutical ingredients and drug candidates that are improving pharmaceutical productivity. The academic drug discovery institutions joined the drug development landscape at the same time. The Pharmaceutical industry has recognized the potential of the combination of cutting-edge academic research and has expanded its effort to engage academic drug discovery institutions. These interactions are either in the form of traditional sponsored research programs or other forms of collaborations that have been established.

Pipeline enrichment

Big pharma has expanded their bio-pharmaceutical efforts and a significant number of new drugs approved in the last 5 years have been therapeutic proteins and antibodies. Many of these biotherapeutics represent “personalized medicine” approaches and hold promise for specifically targeting diseases. 

Personalized Medicine

The role that a patient’s genetic makeup plays in the efficacy (pharmacogenomics) and safety (pharmacogenetics) experienced by the patient became a focus of pharmaceutical companies and regulatory agencies as well. Considering the increase in the cost of health care, personalized medicine will revolutionize health care and will lead to effective diagnostic-tools, which will give early prediction of diseases and lead effective preventive and therapeutic intervention.

Computational Tools

Modern industrial research facilities depend on IT tools to drive productivity, interpret data, maintain records and support the coordination of functional teams. Drug discovery scientists are supported by a wide range of innovative IT systems. Computational Tools are now an integral part of drug discovery. The success of any drug discovery project depends on a large extent on the quality of leads which are taken forward into the discovery phase. Any technology which can support this process may have a significant impact on the project, such as the analysis of millions of data points provided by biological screening, using high throughput systems. There are also industry standard tools for the analysis of pharmacokinetics, pharmacodynamics and clinical trial data available to understand how potential new therapeutics behave in an in-vivo setting.

Outlook

Regardless the challenges the pharmaceutical industry is facing they will be continuing with their commitment to innovate and discover further innovative drugs to address unmet medical needs for the treatment of various diseases. The industry will continue to partner with CROs and CMOs to provide Active Pharmaceutical Ingredients (APIs), drug candidates and improve pharma productivity as well as partnering with academic institutions to have access at an early stage to innovative drug candidates to address the innovation gap. The partnership between pharma and academia is being expected to continue to grow during the next decade as many academic drug discovery institutions are well prepared for doing the early work around target discovery and validation.

 

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