Monthly Archives

May 2021

Big Data Analytics in Pharma Industry

By | Blog

In the current world, where COVID 19 has showcased the rate of transmission of a virus worldwide, there is a clear ask for innovative technology solutions to speed up the processing of drug discoveries. As pharma companies raced towards minimizing timelines for vaccine discovery, which usually takes 5-10 years, it has now been done within a couple of years. What changed exactly? The answer is Data Analytics.

Data has always been the pedestal for drug discoveries to identify the sequences and theories about the efficiency of the treatment. But as more and more factors are introduced in the picture and huge amounts of data are being generated, to an extent where it is humanly impossible to process it within smaller time frames, technology comes into play. The term is coined as ‘‘big data’’, which means large and complex data sets that are difficult to process using traditional database technology. But big data is not just about the size or amount of data, and it comprises 4 dimensions, Volume, Velocity, Veracity, and Variety.

The Four Dimensions of Big Data

Big data unlocks the true potential for data analytics, from accelerating drug discoveries to understanding patient trends and behavior. Big data unlocks the true potential for data analytics, from accelerating drug discoveries to understanding patient trends and behavior.

Here are 4 ways that pharma companies use Data Analytics to drive innovation.

1. Drug Discovery and Development

Applying predictive analytics to the search parameters should help them hone in on the relevant information and also get insight into which avenues are likely to yield the best results. Pharmaceutical Companies like AstraZeneca, Celgene, Bayer, Janssen Research and development, Sanofi, and Memorial Sloan Kettering Cancer Center, started a data-sharing initiative under the name Project Data Sphere to share previous research data on cancer to help experts in their research on treatments against the disease.

2. Targeting specific population segments

Scrutinizing data sets from various sources allows researchers to identify patterns that provide them with key feedback on how infections perform concerning specific population segments.

3. Customized patient care

Companies can leverage data from devices, which provides insights into the current patient behavior through analytics models. According to the formulated insights, the medical practitioner or companies can use this information to design services targeted to different demographics and curate treatments.

4. Streamlining Clinical Trials

Big data can assist the appropriate candidates for clinical trials by analyzing demographics and historical data, remote patient monitoring, due diligence of the previous trails track record, and even predicting outcomes of the trial. In addition, big data analytics can further narrow the patient funnel by considering more factors such as genetic information that help streamline clinical trials and drive down costs.

As the pharma companies are looking to drive maximum ROI before their patents expire, data analytics which substantially reduces timelines plays a key role in driving value. Not only does technology derive enhanced results, but it also provides a competitive advantage.

Artificial Intelligence Trends in Biotechnology

By | Blog

‘Data is the new oil’, we can hear this term being thrown around. Generating large amounts of data was never a problem. The difficulty was in making that data organized and available in an actionable format – in shorter time duration. However, the real challenge has been driving meaningful insights from that data – which is being addressed with the help of AI and Machine Learning.

The application of AI has moved past the general applications as we can see it making groundbreaking advances in the pharmaceutical and healthcare industry.

1. Drug Discovery

The COVID pandemic highlighted the need for faster drug development procedures, and the pharma companies delivered. In a global setting where disease transmission is quick, the need to expedite drug discovery procedures is important. Pharma and Biotech companies are leveraging the abilities of AI for rapid drug development. Once the genes and proteins associated with diseases are identified, the cognitive ability of AI allows thousands of chemicals to be screened to recognize potential drugs that affect the disease. The vaccine or drug development that would previously take on an average of 5-10 years has been reduced to 2-3 years with the help of AI and Data Analytics.

2. Digital Biomarkers

Digital biomarkers are making it easier to analyze data and predict or influence healthcare outcomes. As patient data is being increasingly harvested digitally, the opportunities to implement artificial intelligence (AI), especially machine learning, are increasing exponentially. Recently, the FDA issued the emergency use of the first machine learning-based COVID-19 non-diagnostic screening device called the Tiger Tech COVID Plus Monitor. The device recognizes certain biomarkers that may be indicative of SARS-CoV-2 infection as well as other hypercoagulable conditions or hyper-inflammatory states in asymptomatic individuals over the age of 5.

3. Gene Editing

Gene editing has made it increasingly possible to alter DNA sequences in living organisms in turn customizing the gene expression. The main applications of AI in gene editing are the identification of harmful genes and the treatment of diseases. AI reduces concerns regarding human errors with gene editing and is recognized to improve the procedure accuracy and yield better results.

While there are drawbacks to gene editing, AI and Machine Learning have tremendous potential to make gene editing more efficient and accurate. This will influence pharmacogenomics, genetic screening tools for newborns, enhancements to agriculture, and more.

4. Precision Medicine

he role of AI in Precision Medicine development is promising a new future of personalized healthcare. AI-enabled reinforcement learning allows sophisticated computation to generate insights, and empower clinicians to make well-informed decisions. On the other hand ‘precision therapy’ can be coined as a term as well, where we can see AI and ML algorithms being trained according to certain common health conditions, which enable caregivers to outline a therapy for patients suffering from mental health issues based on insights.

Data is critical for drawing insights, and technology is bridging the gap between this data and actionable intel. While the above-given trends are only the tip of the iceberg, AI & ML are expected to dramatically improve health outcomes – whether in the research of new drugs or the delivery of clinical care.