How data is transforming business for pharma industry
Data integration is a key touchstone for conducting scientific investigations with modern platform technologies, managing increasingly complex discovery portfolios and processes, and fully realising economies of scale in large enterprises
The ability to effectively integrate data and knowledge from many disparate sources will be crucial to future drug discovery. Data integration is a key touchstone for conducting scientific investigations with modern platform technologies, managing increasingly complex discovery portfolios and processes, and fully realising economies of scale in large enterprises.
However, viewing data integration simply as an ‘IT problem’ underestimates the novel and serious scientific and management challenges and potential that it addresses. Challenges could require significant methodological and even cultural changes in our approach to data.
New drug discovery is a complicated, expensive and time-consuming process. Traditional drug
development pipelines need 12-14 years and $2.7+ billion on average to result in a single successful outcome.
This effectively reduces the ‘productive life’ of a patent and calls for further investments later, in
greening and other ways to exploit patents. The smaller window to recover these investments
makes drugs expensive, and, thus, also limits the viable market size.
Reducing research costs and speeding up development processes of new drug discovery are challenging issues for the pharma industry, some answers have emerged and found validation in the recent pandemic.
The rapid growth of computational tools in recent times, such as Computer-Aided Drug Discovery
(CADD), has made significant impact on drug design. CADD enables faster, cheaper and more
effective drug design, and provides fruitful insights in the area of therapy.
Every stage of the drug discovery process produces tons of disparate data. With the arrival of
Artificial Intelligence (AI), the design of drugs ‘in-silico’ has brought about unprecedented changes.
State-of-the-art deep learning approaches, such as retro-synthetic routine planning, drug scaffold generation and drug binding affinity predictions have the potential to produce excellent chemical properties needed for new molecules.
The application of Bioinformatics across the various stages of the drug discovery, thereby, reduces
the risk of failure, makes the process cheaper and reduces turnaround times and reduces human
intervention and error by automating processes.
The Target Identification Process is greatly optimised by bringing together the knowledge of molecular bases for the disease and virtually screen targets for compounds that bind and inhibit
the protein.
High throughput screening is the traditional method in in lead identification. Bioinformatics helps screen target proteins against a database of molecules to see which compound binds strongly with the targets.
Quantity Structure Activity Relationship (QSAR) is the computational technique employed to refine the structure of the lead compound. The information from QSAR can be used to suggest
new chemical modifications and testing.
The pre-clinical testing phase benefits from bioinformatics by making it possible to do testing
without the use of animals and involves pharmacology, toxicology and pharmacokinetics.
Clinical trials are then conducted to establish Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) and efficacy. The ability to predict these parameters in advance with bioinformatic tools like C2-ADME, TOPKAT, CLOGP, DrugMatrix, AbSolv, BioPrint, Gastroplus is a significant enabler to decision-making and lab processes becoming more efficient.
Genomics, proteomics and biopharma research have the potential to yield many more, and targets with greater specificity leading to personalisation of medicine; while virtual screening has the ability to bring predictive abilities to drug development. Combinatorial chemistry with molecular modeling allows producing a vast number of compounds and models, and improve activity using computer graphics and other bioinformatics methods.
The power of in-silico, tissue and computer-based models to pre-clinical testing and the application of Artificial Intelligence (AI) will change the clinical trials just like it is already impacting EMR.
The application of AI to EMRs closes the loop on the healthcare value chain and brings value to
the drug development cycle, thus, benefitting mankind in terms of both health and economics.
The ‘Deepmind’ protein folding is an open source tool from Google that is beginning to
revolutionise the drug target a