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Emerging revolution transforming Life Sciences, Biotech and health care industry through Smart Biomanufacturing and other technologies

Health life sciences” refers to the application of biology and technology to improve health care, and includes biopharmaceuticals, medical technology, genomics, diagnostics and digital health. In general, biotechnology is the utilization of biological procedures for industrial and other purposes, specifically, the genetic management and the manipulation of microorganisms for the production of antibiotic drugs, hormones, and medical devices.

 

The sector generates a wide range of products including drugs, medical technology, diagnostics and digital tools. Globally, life expectancy has increased by more than 6 years between 2000 and 2019 – from 66.8 years in 2000 to 73.4 years in 2019. While healthy life expectancy (HALE) has also increased by 8% from 58.3 in 2000 to 63.7, in 2019, this was due to declining mortality rather than reduced years lived with disability.

 

Globally, healthcare expenditure is growing rapidly. According to a report published by the World Health Organization (WHO), global health spending increased from USD 7.6 trillion to USD 7.8 trillion. Furthermore, it stated that the cost of healthcare also increased drastically. Life science analytics can help in reducing healthcare costs in many ways. It can aid in reduction in clinical decision time or faster time to treatment, improved performance of healthcare professionals, risk mitigation, reduced hospitalization and readmissions, customized medication, and elimination of unnecessary testing. This in turn is estimated to drive the market.

 

The life sciences and healthcare industry as the name suggests is an amalgamation of industries like hospital management, Pharmaceuticals, Health insurance companies, Donors, Manufacturer of Medical equipment etc. This industry is supported by great professionals like doctors, therapists, psychologists, biotechnologists, nurses, midwives etc. The main purpose of this industry is to provide the best possible treatment, curing of the patients, health-related benefits like death claim, life insurance policies.

 

The industry is also facing new challenges: Populations are aging, Chronic illnesses are increasing, New disease strains are emerging at an alarming rate. Add to this mix, the soaring number of patients in a greater spread of geographies.   As per the 2011 national census , in India 68.86% people residing in rural areas still don’t have access to quality healthcare. This population pool is devoid of sufficient ambulatory, clinical, and hospital care facilities.

 

The life sciences sector has played a pivotal role amid the COVID-19 pandemic. To cope with the global crisis, traditional competitors partnered to accelerate research and develop the fastest novel vaccine in the history. Governments, health systems, payers, retail pharmacies, and nonprofits are now working collaboratively with the sector to provide widespread distribution and administration.

 

Corporate funding for digital health reached a record US$21.6 billion globally in 2020—an increase of 103% over 2019. One thing is clear—with the help of digital health tools, virtual care can fundamentally change health care access and deliver an improved care experience. Digitization in the life sciences sector has also led to an increase in new point of care systems, digital pharmacy setups, and easy and efficient access to health care.

 

To keep pace with a rapidly changing technology landscape, organization would need to develop a deeper integration, collaboration, and synchronization of activities across all channels.

The avalanche of scientific and technological innovations

Industry 4.0 and the smart manufacturing movement now provide biomanufacturing the opportunity to upgrade its production processes to be in harmony with the latest industrial revolution. Due to the promise of increased productivity and flexibility, there is significant interest from both managers and process engineers to transform their plants to smart manufacturing facilities.

 

The Life Sciences industry is undergoing a major transformation. A large part of this is fuelled by the integration of digital and ICT technologies. This transition has opened up new opportunities for development, but also comes with its own challenges. Technology is playing a massive role in enabling the industry, be it analytics in personalized medicine, cloud computing in collaboration, or wearable devices in remote and self-health monitoring. Report predicted that in 2030, technology companies will be key players and provide crucial input by leveraging new technologies such as AI, cloud-based platforms, machine learning, cognitive technology and wearables.

 

A range of new capabilities and platform technologies are emerging, which will drive change along the entire R&D spectrum. New scientific technologies such as cellular, gene and tissue engineering will play an increasingly important role in the healthcare paradigm.

 

Research is ongoing to use stem cell therapies to reduce or even eliminate the need for surgery in areas such as cardiology and neurology. Genotyping helps to predict the propensity of a disease based on one person’s genes. Coupled with gene editing technology, genomics is expected to play a significant role in transforming the R&D landscape, identifying both new therapies, as well as replacing existing treatments with curative therapies.

 

The increasing focus on systems biology has allowed unpreceded insight into the mechanisms of diseases, resulting in the discovery of novel drug targets to interfere with and slow or halt the process of disease.

 

Molecular biology continues to evolve exponentially which will enable more targets to be identified. Ex-vivo models are becoming increasingly more sophisticated and will not only speed up R&D, but will also make it more cost effective and increase precision. For example, Midbrains (stem-cell-based ‘brains in a dish’) show great potential for the replacement of animal brains for preclinical testing

As the world becomes increasingly connected, information and communication technologies will fundamentally reshape both the consumption and delivery of services in life sciences. Network connectivity, including access to fast, secure Wi-Fi, is widely available, enabling patient records and point-of-care diagnostics to be accessed as and where needed. ICT and EHRs can together result in the improvisation of quality, safe and efficient healthcare delivery system.

Telehealth

In spite of having a considerate increase in the medical staff and doctors, Rural Indian healthcare system is still facing challenges to attract and retain the expert medical practitioners. Another challenge which has become Achilles’ heel for the Indian rural healthcare is lack of Investment and Infrastructure.

 

Telehealth use is accelerating. By 2018, 65% of interactions with health care facilities will occur by mobile devices. Some 80% of doctors already use smartphones and medical apps in health care provision. Doctors are turning to wireless devices such as Fitbits to understand the factors that help the recovery of patients. This is facilitating regular health monitoring, management of chronic diseases, and post-operative follow-ups.

Predictive and preventive healthcare

Analysing the individual’s genome and microbiome, combining this information with regular laboratory tests (blood, urine, saliva) and monitoring their lifestyle and physical activity will allow detection of the transition from wellness to early disease. This enables much earlier treatment than is possible today, and the potential to reverse the progression of disease.

 

Tools designed to gain insight into individual health situations also enable predictive and preventive healthcare. This will be achieved by encompassing data on genomes, proteomes, epigenomes, transcriptomes, phenomes, metabolomes and behaviour.

 

Personalised Medicine

The access to real-world data, coming from electronic health records, mobile apps, diagnostics, genomics, as well as data analytic tools, will help to further support the development of these personalized treatment options. Online portals enable regulatory-compliant video interactions between the patient and clinician.

 

As the life sciences industry’s research body becomes increasingly expansive, many experts discover that some conditions are inherited through genetic coding. This implies that healthcare can be more customized based on DNA information or other genome features. This ushers a new era of personalized medicines and healthcare procedures. The Genome Asia 100K project aims to sequence 100 000 genomes from various Asian ethnicities within three years, and to build a genomic variant database that will enable precision medicine applications.

 

Incorporation of genetics in treatment.

There will also be an increase in integrating genetic information in the assessment and treatment of disorders. Additionally, gene technology will enable researchers to identify genome sequences that predict disorders in humans and animals. At present, there are experiments in the area of gene editing that could potentially prevent diseases and other conditions that can be inherited from the mother to the child. These types of studies will continue to rise as more conditions are discovered to be genetic in nature.

 

Improvement of drug approval timelines.

Much-needed medications are being held stagnant due to the long process of drug approvals in the FDA. However, advances in technology will help the government bodies improve drug testing speed and hold controlled trials for candidate patients. An example of this is the Real-Time Oncology Review (RTOR) pilot, which is aimed to accelerate the drug approvals related to cancer treatment.

 

IoT and Wearables

There is now broad adoption of bio-sensing wearable devices (interoperable, integrated, engaging and outcomes-focussed) – the technology has become much cheaper and more sophisticated, and the quality of data has improved.  New generation wearables continuously monitor a broad range of physiology, with service users supported to use the data to improve prevention. Wearables and connected devices are helping seniors age in place, alleviating, to an extent, the growing care gap

 

Ubiquitous presence of smartphones and substantial investments in Internet-of-things (IoT) are providing an exciting opportunity to reduce the gap between the patients and the pharmaceutical industry.Today, India boasts of nearly 1 billion smartphone users in the country. The widespread network of existing smartphones can be used to build an inclusive IoT (Internet of Things) network to create comprehensive EMRs for rural patients

 

The higher adoption of IoT has already started to facilitate at-home diagnostic testing, self-management of chronic diseases, and remote patient-health care provider interaction in the healthcare industry.

 

Early market movers already see the use of pill-shaped micro-cameras that traverse the human digestive tract, sensors in pills that track concordance, hip replacements that detect falls and send messages to care providers, and thousands of health-monitoring applications that send messages and data from the home to the hospital or patient to the HCP to improve early diagnosis and treatment solution.

 

The adoption of IoT is yet to pan out in the life sciences industry. The industry must work cohesively to overcome the barriers to wearable technology adoption – concerns of security and privacy, data sharing and protection, regulatory compliance, among others.

 

Cloud for Data management and integration.

In the early stages of data management, it was difficult for data analysts to gather, organize, and interpret information that is pertinent for life sciences research and system operations. The innovation of cloud management will enable data management professionals to easily collect and interpret information for improved life science practices.

 

COVID-19 has accelerated new ways of working in the life sciences industry that have been talked about for years—chief among them a shift to patient and customer-centricity, digital interactions, and workforce agility. Almost overnight, R&D teams reprioritized new research, plant and network experts rallied to ensure clinical supply continuity, and go-to-market leaders shifted to enable at-home medical field force.

 

According to data from Netskope—a provider of cloud security services—by the third week of March, around 60 percent of employees started working remotely, up from around 25 percent in the months prior to the COVID-19 outbreak. Even now, after some suspended clinical trials have resumed, more than half of the interactions between the lead physician and patients are done virtually, compared with 8 percent pre-crisis.

 

In pharmaceutical research where large volumes of data (notably next generation DNA sequencing systems  and genomic tools) needs to be  mined and the cost of obtaining this  sequence is rapidly decreasing, data has further increased the number of both, instruments being used and labs using them. Through cloud’s agility of provisioning and pricing (pay-peruse), setting up massive infrastructure resources for data crunching, analysis, or simulation is no longer an impediment.

 

A large pharma company is setting up a cloud-based solution to integrate clinical data across all its global trials and provide it to its global operations team for analysis. These big data solutions that receive clinical data instantly from all the current trails will reduce the time taken to analyze and predict the path of the trials, while decreasing the operating expenses substantially. On a broader application, the scope of collaboration is expanding to include R&D processes outsourcing, exemplified in virtual laboratories where thousands of researchers from contract research organizations can seek and provide help

 

Big data analytics

One of the trademarks of Industry 4.0 is big data, which refers to large sets of process and product data collected by sensors and process analytical technologies (PAT). Among several other benefits, integration of data from operations and business activities can promote productivity by allowing greater visibility across upstream and downstream operations. Being able to use historical and real-time data to predict future outcomes is an empowering tool that can help employees to be proactive instead of reactive. They can understand in an agile manner what is happening in a process and why, as well as predict what will happen when variations occur

 

New data analytical methods for extracting clinically relevant knowledge are needed. The use of artificial intelligence is expected to be instrumental in delivering decision support for medical professionals and patients, as well as for healthy individuals. Data analytics are the key to provide real-time insights, as well as enabling evaluation and validation of all critical process parameters against regulatory guidelines, ranging from raw materials to the finished product. This actually helps companies, especially within the biopharma sector, to comply with the strict and compulsory requirements that are characteristic of that sector.

 

Since early 2000, research units within biopharmaceutical organizations have been actively harnessing the powers of big data by leveraging the advancements in next-generation sequencing. This includes a variety of studies including whole-genome sequencing, targeted re-sequencing, discovery of transcription factor binding sites, and noncoding RNA expression profiling, among others. Organizations are now able to leverage the vast library of available molecular and clinical data, utilize predictive modeling techniques, and identify new potential candidate molecules with a high probability of being successfully developed into drugs while ensuring efficacy and safety.

 

AI and Machine learning

Machine Learning (ML), a branch of Artificial Intelligence (AI), is one of the ways to achieve this. ML works with small to large datasets by analyzing and comparing the data so as to find mutual patterns and explore differences

 

Over the last decade, early business uses of Artificial Intelligence (AI), or machine learning, in life sciences have proven successful in drug discovery – predicting molecule-target bonding, identifying new biomarkers, and uncovering new drug indications. Now machine learning is gaining broader traction into other areas, including commercial operations, and transforming the way the industry collects, synthesizes, and uses data.

 

Smart Biomanufacturing

The race for smart biopharma processes is driven by the emergence of three trends: personalized drugs, often tailored the genome of the patient, as a new treatment option for both rare and widespread diseases; flexibility as a means to make plants smarter and adaptable and finally using digitalization to acquire a deep level of process understanding. The industry increasingly relies on small-scale production lines for both testing and optimizing existing processes and for developing new procedures and performing pilot scale productions.

 

Smart factories hold the promise to also increase sustainability through real-time monitoring of production, where the automated control systems are expected to reduce the number of faulty batches and cut the maintenance costs. Being able to rely on automated systems that require minimal human/manual intervention will result in higher yields and quality, alongside with decreased costs and waste generation, which is of great importance to bio-based production, and especially biopharma.

 

Notwithstanding the different objectives and end products, most biomanufacturing processes include the cultivation of microorganisms, which implies a process consisting of complex chemical, physical and biological phenomena. A dependable and consistent analytical system is necessary to control the process conditions in all parts of the biomanufacturing process (upstream, downstream and product formulation).

 

To improve yield, reduce time to market and facilitate process validation, Smart Biomanufacturing processes must deliver insights into biological behavior as well as process and product quality. Digitalization enables a deep process understanding for continuous, real-time quality control.

 

However, many biomanufacturing companies, especially in the biopharma sector, still have a long way to go to fully benefit from smart manufacturing as they first need to transition their current operations to an information-driven future. One of the most significant obstacles towards the implementation of smart biomanufacturing is the collection of large sets of relevant data.

 

3D printing

3D printing is another promise of a new global industrial revolution as well as an opportunity to customize patient treatment. For biologics, 3D printing is being explored as a better way to manufacture cell and tissue products. Drugs and disease models can be tested on 3D-printed tissues instead of on animals or humans.  In manufacturing, 3D printing has the potential to lower costs, increase production speed and flexibility, minimize distribution borders, and create new markets worldwide.

 

 

Blockchain

Interest and investment in blockchain has grown exponentially across a large number of sectors, but particularly within both Life Sciences and Healthcare. This is not surprising given its potential to transform existing business and operational models, creating operational efficiencies, as well as new revenue opportunities.

Pharmaceutical companies have been exploring blockchain use cases in:

—Patients, focusing on consent and permission within business processes such as managing the patient journey; clinical trials; electronic health records; and recall management.

—Regulatory/compliance, focusing on data integrity, audit trail and traceability in business processes including laboratory instrumental data; preventing counterfeit drugs; and employment of cross-border healthcare professionals.

—Interoperability, focusing on provenance and data sharing in business processes such as supply chain; contract management; and the cool chain

 

Quality Training for Quality Treatment

Another issue of specialized medical training can also be resolved through ICT, AI (Artificial Intelligence), and VR (Virtual Reality). Training for emergency situations can be simulated through VR to train medical staff and equip them with cloud-based advanced equipment to record patient’s vitals and relay to remote doctors for expert investigation. This can efficiently resolve the issues arising because of lack of doctor-population ratio and proper infrastructure in remote rural areas.

 

Global Market

The global life science analytics market size was valued at USD 7.2 billion in 2019 and is expected to expand at a compound annual growth rate (CAGR) of 7.9% from 2020 to 2027. The emergence of advanced technologies in the life sciences industry, the rise in demand for personalized medicines, and the increasing impact of the internet and social media on life sciences companies are some of the key factors driving the market.

 

The increase in adoption of life science analytics in various applications, such as reporting of adverse events, management of Pharmacovigilance master data, brand reputation analysis, customer satisfaction analysis, tracking spending activity, is one of the key factors that is expected to propel market growth over the forecast period. In addition, factors, such as first call resolution analysis, reimbursement calculations, tracking of patient health outcome, reporting drug effectiveness, and budget estimation for new drug development, are among some other factors expected to drive the market potential. Growing awareness amongst the end-users regarding the benefits of big data analytics, such as focused sales and marketing analysis, pharmaceutical innovation and drug safety trials, and safety analysis are among few factors that are driving the market.

Growing demand for improved patient outcomes is also anticipated to drive the market for life science analytics over the forecast period. Proper analysis and streamlined processes are improving operational efficiencies and patient outcomes. Some of the key examples are population health management with self-service analytics. Real-time analytics boosts the overall productivity and revenue cycle management by automating ad hoc visual analysis.

The rapid adoption of analytics in clinical trials is also contributing to market growth. Life science companies use analytics to enhance the efficiency of clinical trials and improve clinical trial data management. According to a survey conducted by ERT in May 2020, a provider of clinical services and medical devices, 82% of organizations are incorporating virtual clinical trial technology. Such adoption of advanced technologies is expected to drive the market.

 

 

 

 

References and Resources also include

https://www.drugpatentwatch.com/blog/top-6-issues-facing-biotechnology-industry/

https://www.infosys.com/industries/life-sciences/white-papers/Documents/life-sciences-industry.pdf

http://www.businessworld.in/article/The-Roadmap-For-Quality-Healthcare-in-Rural-India-Through-Technology/16-12-2018-165227/

 

 

About Rajesh Uppal

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