Pharmaceutical companies are exploring means to faster the steps in pharmaceutical development i.e. to go from the pre-clinical phase to clinical trials as efficiently as possible. In terms of efficient lab operation and R&D workflows biotech companies are implementing following in trend ways to speed up the drug development process in their pipeline. The clinical trial industry is evolving at a rate that is difficult for pharmaceutical corporations to stay up with.[1]

NLP Text Mining Automation

The massive amounts of data they handle and the steady stream of new knowledge they learn are impeding their old procedures. There is a solution provided by automation. Automation can increase trial efficiency, improve patient recruitment, and speed up data analysis by optimizing workflows, 

decreasing manual chores, and eliminating human error. The long-term advantages of automation outweigh the time and effort required to deploy it. It has the potential to accelerate medication development by freeing up funds for fresh initiatives. Most common in use is target identification and 

validation using AI-powered literature mining, such algorithms are able to quickly scan through a large body of scientific literature to find possible drug targets. Using high-throughput computer techniques, in silico screening can find potential candidates by digitally screening millions of chemicals against target proteins. Businesses in the bio sciences are using natural language processing (NLP)to go from molecules to markets.[2] For example, Pfizer's capacity to monitor rival activities and pinpoint possible therapeutic targets has been greatly enhanced by their patent search service driven by NPL. Pfizer was able to decrease human labor, improve data comprehensiveness, and speed up the discovery of new insights by automating the process of extracting important information from patent filings. Across the enterprise, this solution has shown to be a useful resource for researchers and decision-makers.[2] 

Other than that, through the extraction of important data from preclinical safety reports, Merck's NLP process offers researchers a thorough understanding of pertinent information. Merck can lower the possibility of late-stage failures and more accurately evaluate the applicability of preclinical data to human safety by looking at the interpreted outcomes sections. With the aid of this technology, Merck has been able to make better selections and increase the general effectiveness of their drug development procedure.[2] Eli Lilly use NLP to examine the connections between publication volume

and the effectiveness of medication development. Likewise companies are using NLP for developing the robust pipeline for indications with the evidence based real world data.[3]

Figure 1. AI based Natural language processing (NLP) text mining is executed for drug development process while screening patent filling, preclinical safety report, liteature to scientifc breaktrough and drug approvals Improving trials through digital health technologies (DHTs)

Improving trials through digital health technologies (DHTs)

Patient trial experiences can be greatly improved by digital health technology. Pharmaceutical firms may enhance patient recruitment, participation, and overall happiness by using these technologies from the outset of their studies. Software for remote collaboration can help expedite lab staff procedures by guaranteeing prompt access to patient data and avoiding delays. The use of digital health technologies (DHTs) in medication development has a lot of potential advantages. DHTs can improve clinical studies by gathering real-time data from patients and providing more accurate and thorough information. The FDA has created a framework to direct the use of DHTs in medication development and is dedicated to promoting their usage. Workshops, demonstration projects, and the release of guidelines are all part of this framework. For stakeholders looking for information about DHTs and their regulatory status, the CDRH Digital Health Center of Excellence is a great resource.[4]

AbbVie is dedicated to developing new treatments for patients by concentrating on illnesses with significant unmet needs. To make sure that patients' opinions are heard at every stage of the Natural language processing (NLP) AIpowered literature miningPatent filingsPreclinical safety reportsScietific breakthroughs & Drug approvalsmedication development process, they collaborate with patients and other stakeholders. Importantareas of attention consist of drug development that is patient-centered involves working with patients to comprehend their needs and experiences. By diversifying clinical trials and ensuring that the communities being studied are represented in the trials. Comprehending the psychological and practical trajectories of patients by integrating the patient experience. Focusing on developing digital health technologies by applying technology to enhance clinical evaluations and gather data. Hence establishing Real-World Evidence's (RWE) by incorporating real-world data into conventional clinical studies main got more reliable and structured. Through this AbbVie hopes to create cutting-edge treatments that meet patients' demands and enhance their quality of life. [5]

Inter-Industry Partnership

The foundation of a successful drug development program is collaboration. Collaboratively, scientists from government, business, and academia may combine knowledge, resources, and data to speed up the search for and creation of novel treatments. Teams may exchange expertise, cut down on duplication, and improve the effectiveness of the drug development process by forming partnerships. Collaboration may also encourage a more varied and creative approach to problem-solving, which might result in innovations that might not have been achievable while working alone. For example, launched in 2021 from Alphabet's DeepMind, Isomorphic Labs is an independent startup that aims to expand on the success of AlphaFold, the firm's revolutionary work in protein folding. It has signed a $45 million upfront payment agreement with Eli Lilly and Company for its first strategic research cooperation, with a potential total deal value of up to $1.7 billion. Additionally, it has partnered with Novartis on a strategic research project to find small molecule treatments for three unidentified targets. Novartis has paid it $37.5 million up front to work on the small molecule therapeutics discovery project.[6] Likewise, a multi-year research cooperation between Evotec and Pfizer will concentrate on early discovery research for infectious and metabolic illnesses. Pfizer will support Evotec and perhaps provide milestone payments for the study, which will be carried out at its facilities in France. [7]

Continuous manufacturing

There are several advantages to continuous production over batch processing. Because it allows all units to operate simultaneously and eliminates hold periods, it can increase production, lower costs, and shorten time to market. For instance, the Novartis-MIT Centerfor Continuous Manufacturing was a platform for collaboration between Novartis and MIT. The pilot plant, which could make tablets from raw materials with a two-day lead time—a process that would take two hundred days in batch—was the project's high point. Spun out of MIT by Continuus Pharmaceuticals, integrated continuous manufacturing (ICM) is the cutting edge production platform that made this ground-breaking innovation possible. [8]

To sum up, the pharmaceutical sector is changing quickly in response to the growing need for effective medication development. Technological developments like digital health, artificial intelligence, and continuous manufacturing are vital in quickening the process. Pharmaceutical businesses may expedite the launch of novel treatments, improve patient experiences, simplify operations, and analyze data better by utilizing these advancements.

References:

1. ZAGENO 6 Time-Saving, Tech-Driven Methods for Speeding up Drug Development. Posted Jul 25, 2023; Accessed on Sep 16,2024 URL: https://go.zageno.com/blog/six-time-saving-tech-driven-methodsspeeding-drug-development

2. Drug delivery & development NATURAL LANGUAGE PROCESSING - How Life Sciences Companies Are Leveraging NLP From Molecule to Market. Issued on Apr 2020; Accessed on Sep 16,2024 URL: https://drug-dev.com/natural-language-processing-how-life-sciences-companies-are-leveraging-nlpfrom-molecule-to-market/

3. Jane Reed, Copyright Clearance Center Published on Nov 12, 2019; Accessed on Sep 16, 2024 URL : https://www.copyright.com/blog/ai-powered-text-mining-research-insights-scientific-literature/

4. Digital Health Technologies (DHTs) for Drug Development, FDA Issued on Aug 8, 2024; Accessed on Sep 16, 2024 URL: https://www.fda.gov/science-research/science-and-research-special-topics/digitalhealth-technologies-dhts-drug-development

5. Patient-Focused Drug Development, Abbvie Accessed on Sep 16, 2024 URL: https://www.abbvie.com/science/areas-of-innovation/patient-focused-drug-development.html

6. Isomorphic labs Released on Jan 7, 2024; Accessed on Sep 16, 2024 URL: https://www.isomorphiclabs.com/articles/isomorphic-labs-kicks-off-2024-with-two-pharmaceuticalcollaborations

7. Evotec Released on Jul 10,2024 ; Accessed on Sep 16, 2024 URL: https://www.evotec.com/en/news/evotec-and-pfizer-collaborate-to-advance-drug-discovery-in-france

8. CHEManager Published on Mar 20, 2024; Accessed on Sep 16, 2024 URL: https://www.chemanageronline.com/en/news/integrated-continuous-manufacturing-pharmaceutical