Tag: Drug Discovery

Artificial Intelligence in Drug Discovery – Could help Reduce Significant Capital and Time Required

Drug discovery is a complex process which involves significant utilization of time and resources. As per several sources, on an average, the entire drug development process (from initial proof-of-concept to commercial launch) takes around 10-15 years and capital investments worth USD 4-10 billion to develop a drug from concept to commercial launch. The early stages

REAL-WORLD EVIDENCE: THE GAME CHANGER IN DRUG DEVELOPMENT

While the definition of real world evidence is still evolving, most proponents associate it with data that is derived from medical practice among heterogeneous sets of patients in the real world setting. It is the integration of data gathered from various sources, such as EHRs, medical charts, patient claims and billing activities, product and disease

Increasing Rate of Drug Failure has Prompted the Drug Developers to Rely on CROs offering In Vitro ADME Testing Services for their Outsourcing Requirements

It is important to note that the process of drug discovery is extremely demanding, both in terms of capital and time. In fact, the overall amount spent on R&D initiatives in the pharmaceutical / biotechnology sector has increased from around USD 128 billion in 2008 to USD 165 billion in 2018. Moreover, only a small

Rising Popularity of Drug Targeting Synthetically Lethal Targets Fuels the Battle of PARP Inhibitors for Treatment of Advanced Cancer Indications

Cancer is considered to be the second leading cause of mortality, after cardiovascular diseases, accounting for every sixth reported death in the world. The International Agency for Research on Cancer (IARC) states that the number of new cancer cases is expected to grow to 27.5 million across the globe, by 2040. Experimental evidence has shown

Targeted Protein Degradation – Defining a New Frontier in the Field of Medicine

The concept of targeted protein degradation presents revolutionary drug development opportunities and is anticipated to bring about a paradigm shift in modern healthcare. While conventional medicines, such as small molecule inhibitors and monoclonal antibodies, address fewer than 20% of the proteome, targeted protein degradation offers a unique means to tap into the rest of the

In Silico / Computer-Aided Drug Discovery Service Providers for Large Molecules

Over time, the complexities associated with drug discovery have increased, especially in case of large molecule drugs, which are inherently more complex than conventional small molecules. As a result, an increase in the overall research and development (R&D) expenditure in the pharmaceutical / biotechnology sector has been witnessed. In addition to the complexities involved, the

Computational / In Silico Drug Discovery: A Boon in Disguise for Faster Drug Discovery Operations Amidst COVID-19

The COVID-19 pandemic is predicted to throw the global economy into recession, with loss of trillions of dollars. The pharmaceutical industry cannot escape the brunt of the circumstances as well. In fact, according to a survey, 95% of healthcare industry professionals are worried about the impact the pandemic is likely to have on the industry. So far,

Fragment-based Drug Discovery – A Boon in Disguise to get a Breakthrough Drug Against Covid-19

With over 855,000 cases and 42,000 deaths, the novel coronavirus has taken its toll on humanity. The pandemic is predicted to throw the global economy into recession, with loss of trillions of dollars. The pharmaceutical industry cannot escape the brunt of the circumstances as well. In fact, according to a survey, 95% of healthcare industry

Deep Learning: Opportunities in Drug Discovery and Diagnostics

Deep learning is a novel machine learning technique that can be used to generate relevant insights from large volumes of data. The applications of the technology are being explored across a variety of areas. The ‘Deep Learning: Drug Discovery and Diagnostics Market, 2017-2035′ report examines the current landscape and future outlook of the growing market of deep learning