Amazon Omics: A new age of clinical research is rising
Discover the latest clinical research trends in Amazon Omics, and explore how the phenomenon revolutionizes data analysis and precision/personalized medicine.
in the pharmaceutical
industry: from drug
Digital transformation can be a game-changed when it comes to improving drug development process
Only 1 in 10 medications that enter the clinical trial phase actually reach the market. Can drug development processes be improved? What are the possible solutions?
Implement a well-thought-out data management strategy and ensure elaborate data validation policies with data governance. This article covers the techniques, tools, and technologies that can be used to deliver solutions to long-standing problems in the pharmaceutical industry during the five stages of drug development:
Since the start of human civilization, people have been looking for substances to cure illnesses and infections. In the past, plant-derived extracts and animal cells were used to treat sickness, and the discovery of such treatments has been empirically driven.
However, digital transformation has considerably changed the way new drugs are developed. Evolutionary algorithms, neural networks, natural language processing (NLP), machine learning (ML), wearables, and IoT optimize the workflows in pharmaceutical organizations, automating previously routine and manual tasks and considerably speeding up drug discovery processes.
This article highlights how the advances in digital technology and digital transformation in the pharmaceutical industry have accelerated drug discovery, streamlined clinical trials, eased up drug commercialization processes, and improved drug safety.
As per Eroom’s law, developing a new drug cost roughly doubling every nine years. The most time-consuming and costly phase of developing new drugs is drug discovery. A vast majority of drug discovery studies have yet to be successful. The complete drug development cycle may last ten years or longer to become finalized and may cost over $2 billion.
The discovery of new chemical compounds is an iterative and multi-step process as hundreds or thousands of chemical compounds are evaluated, depending on their primary activity against novel disease-related targets. The drug development process can be seen as a funnel: just a few molecules get through to the succeeding stages.
Various digital technologies and approaches are employed to search for suitable substances with the desired therapeutic effects. Digital transformation brings instruments like neural networks and knowledge graphs for drug discovery, high-throughput screening (HTS), evolutionary algorithms, and genetic programming.
After a period of little progress, Artificial Iintelligence (AI) has experienced a boom in its capabilities. The shift happened when scientists reproduced how biological brains work into the artificial mind. With the striking advances in the ability of machines to understand and exploit data, including texts, images, and speech, many pharmaceutical companies and healthcare professionals can benefit from the mass of unstructured medical data.
Historically, developing an ML algorithm required extensive software engineering expertise to develop feature extractors that modify raw biomedical data into comprehensible representations from which ML algorithms can extract patterns. On the other hand, deep learning is a form of representation learning where an algorithm processes raw data and produces the representations needed for pattern recognition. Deep learning algorithms can process highly complex functions, be scaled to enormous datasets, and continue improving with more data, thus outperforming conventional ML algorithms.
Deep learning technology has already been used to predict the molecular properties of chemical compounds. A remarkable deep learning prediction level of accuracy has already been achieved via a vectorized representation of molecules, saving time spent in the drug discovery phase. In addition, ML helps decrease drug failure rates during clinical development stages, making it a valuable and cost-saving tool.
Neural networks are the most powerful and valuable method in deep learning. They are practical tools for automating tedious and challenging tasks like distinguishing diseased cells from healthy ones, pattern recognition and segmentation of medical images, diagnosis prediction, and disease monitoring. Most importantly, neural networks can:
The neural network classifiers can allocate not only existing chemicals but also generalize related areas of chemical spaces to virtual chemicals. For example, inexistent chemical compounds can be manufactured with neural network predictions determining what chemicals will apply in a particular drug.
Biopharma professionals are going a long way to identify and optimize the lead molecular compounds which can bind to the proteins that cause disease. Genetic programming is a valuable method for the instant identification of the necessary chemical compounds for pharmaceutical drug discovery. In addition, the algorithms can generate new ideas for molecules used in treatments. In this way, researchers can shorten the time of finding the needed molecules so that the treatments can reach the stage of animal and human clinical trials much quicker.
Using these evolutionary algorithms, biopharma scientists can quickly identify synthesizable, stable, and refined molecules for multiple criteria. Moreover, deep learning techniques applied at the early stages of drug discovery can assist with defining which molecules have high ADMET (absorption, distribution, metabolism, excretion, and toxicology) properties. The collected data can later be consolidated into massive-scale compound libraries that can be utilized to speed up the preclinical discovery of needed compounds.
Pharma companies use genetic programming to analyze the molecules to shorten the time spent on drug development. In addition, molecule representations based on quantum chemical calculations enable examining the compound properties to understand better how the molecules may act in a body.
What SARS-CoV-2 triggered is the acute necessity of quick identification of any critical compounds that can halt the global distribution of deadly viruses, now and in the future. In addition, data extracted from scientific literature in structured medical repositories becomes a fundamental instrument for identifying approved medications to inhibit a respiratory infection.
Open and proprietary biomedical data sources allow the collecting, consolidating, structuring, and extracting valuable information when needed. With the help of natural language processing (NLP) and knowledge graphs, previously unstructured raw medical data can be transformed into a contextualized and interconnected representation of critical information.
Applying knowledge graphs to identify the potential treatment for COVID-19 infection is one of the success stories. It became possible to determine what approved medicines could affect the virus directly through raw medical data analysis, stopping the disease’s progression and halting the “cytokine storm” (an immune system overreaction). By analyzing data via the knowledge graph, the prospective medications that can stop the ability of the virus to infect the lungs were identified.
The compound screening process could have been more efficient in the past as it involved empirical observations and random screening. However, high-throughput screening (HTS) technology has considerably improved the compound screening process. HTS allows for a 1,000 times faster compound screening that produces over 100 million reactions in less than 10 hours, with just one-millionth of the cost. In one working day, more than 100,000 samples can be screened for their pharmacological and biological activity, enabling pharmacologists to find the most promising compounds more rapidly.
Current technological and scientific advances are considerably improving the outcomes and quality of the HTS compound analysis, which allows for the collection of complex and complete data in the shortest periods. The essential trends shaping HTS are automation, miniaturization, and artificial intelligence (AI). Automation assists in speeding up the data collection processes. Quality control assists with identifying errors in the HTS arrays. Finally, AI helps glean biochemical significance from the massive data HTS generates. In particular, ML algorithms, such as quantitative structure-activity relationship (QSAR) modeling, can help to determine prospective target molecules from the millions of candidate compounds.
Cloud computing has already changed how technology companies and people do everyday business strategies and tasks. It’s disrupting businesses worldwide, including biotech and pharma organizations, by creating new ways to operate and integrate different workflows, business processes, and tools.
For example, cloud-based computing platforms already assist researchers in conducting virtual compound screenings and high-throughput molecular dynamics. A virtual compound screening may take weeks or months to use central processing units (CPU) in small or medium high-performance computing environments. However, leveraging the CPU power in supercomputers or on-demand cloud resources ensures the virtual screening task will be conducted much quicker, taking only hours or a few days to accomplish.
One of the main reasons behind cloud adoption is its cost efficiency. Cloud-based solutions can quickly scale up and down to address computational demands. As a result, biotech organizations can accelerate their business results using high-performance cloud services and pay only for what they use, thus relinquishing the cloud resources that are no longer needed.
However, cost efficiency is not the only benefit of the cloud. Agility and speed enable biopharmaceutical organizations to fix issues faster, as the cloud servers are accessible from anywhere. Cloud enables the needed specialists to work rapidly and remotely from any place and any device. Furthermore, cloud vendors provide simplified backup/recovery and load balancing options; the cloud vendor’s global infrastructure consists of isolated zones with market access and a low network latency.
One of the key applictions diigital transformation offers is in clinical trials. In most cases, these are laborious and time-consuming, which has a direct impact on the drug development process and drug time-to-market. At this point, it is time to explore how digital transformation in clinical trials is doign it job.
As trite as it sounds, COVID-19 has evidenced the urgency for effective treatments. To deliver the efficacy of medications and do it quickly (another ambiguity), the companies conducting clinical trials strive to engage more patients to reach enrollment targets on time.
Yet, 86% of clinical trials fail to meet their patient enrollment deadlines within the set time frame, state the authors of an article published in Contemporary Clinical Trials. What’s more, the average dropout rate of a drug from a clinical trial is almost 30%.
The inability to find participants on time leads to significant monetary losses that result from slowdowns in regulatory approvals and the late market introduction of products. In addition, with sufficient clinical trial participants, the study has higher statistical power and acceptable study validity, which may bring negative consequences later.
To address the issue of low patient enrollment, a practical solution is to collaborate with influential doctors that can source eligible patients that fit the study inclusion criteria. Digital technologies, such as data science and natural language processing, help to find investigator-influencers that can later help find sufficient numbers of patients for a clinical trial.
NLP is focused on processing texts and natural language to infer meaning from words and texts. NLP methods help to transform raw medical data and enrich it to deliver natural value intelligence to decision-makers, such as identifying influencers among the researchers in their field of interest, evaluating the possibility of carrying out clinical trials in a specific location and finding volunteers to ensure compliance with the trial deadlines and budget. By leveraging NLP, pharma companies benefit from realistic patient recruitment forecasts, achievable timelines, and efficient budget planning.
NLP combined with social graphs can depict connections between doctors researching specific topics. For example, on social graphs, pharmaceutical organizations can quickly see which doctors already work as investigators in a particular clinical trial and which can participate.
Learn more about how we used NLP technologyto increase patient engagement rate for a major US-based life science company. QPharma
Over the last ten years, the adoption of EHRs across healthcare facilities has skyrocketed, partly because HITECH legislation came into force. As a result, the EHR became a new standard for medical institutions by providing many benefits, including reducing errors, enhancing workflow efficiency, and refining healthcare coordination.
Once the health records became digitized, an enormous amount of medical data was aggregated. These large numbers of medical codes reflect various aspects of patient encounters, such as laboratory tests, diseases, medications, and clinical procedures. Initially, these codes were implemented for administrative and billing tasks only. However, they contain important information for secondary pre-processing. Therefore, the latest deep-learning approaches are being used to project different medical codes at a vector pace for detailed data analysis and predictive tasks.
ML algorithms can better leverage information-rich raw data in EHRs. For example, clinical notes should be more frequently noticed when developing predictive systems. Unfortunately, clinical notes are unstructured by nature and require manual review. Still, these notes may contain valuable information, such as admission notes, discharge summaries, and other compilations.
Large-scale recurrent neural networks (RNNs) demonstrate remarkable predictive results by combining unstructured and structured data for semi-supervised learning. Neural networks are pretty successful in the following:
The ultimate goal of processing EHRs with NLP and deep learning techniques is to predict patient outcomes. Currently, there are two types of outcome predictions, such as:
As digitized EHRs resulted in vast amounts of medical data, new opportunities emerged to refine and review diagnosis definitions and boundaries. Since diseases are traditionally characterized by manual clinical descriptions, computational disease phenotyping aims to obtain data-driven definitions of illnesses. ML and data mining techniques can detect more fine-grained illness descriptions. Computational disease phenotyping is a considerable step towards precision medicine and personalized healthcare.
Wearables opened up a whole new world of pharma and life sciences opportunities. Thanks to workforce enablement, they enable 80% faster decision-making, revolutionizing the real-time monitoring of diseases by collecting essential data, such as heart rate, glucose levels, movement disorders, concussions, and other medical events.
Wearables help to decrease healthcare costs by reducing the number of in-person visits to the clinic. The health data collected by a simple medical wearable device can be life-saving. IoT devices can be used to intervene in certain circumstances. In addition, combining the mounds of microscopic edible sensors ingested in our bodies and the ones we wear on our bodies is transforming diagnostic and preventive care as we know it now.
There are numerous successful cases of how wearables are reshaping healthcare. For example, a combination of cloud software and wearable devices can monitor patients’ vital signals and alert medical personnel about potential accidents or falls. This system proved to be so effective in a facility that serves elderly patients that now even the patients’ relatives can remotely monitor the well-being of their family members.
Doctors can spend 6 hours in an 11-hour workday engaged in the electronic health records (EHRs) documentation process, which can cause exhaustion and shortens the time spent with patients. The subsequent generation of information extraction and automatic speech recognition models are expected to be essential components of voice assistants that can accurately transcribe patient visits and reduce the documentation process for doctors.
In addition, based on recurrent neural networks, language translation can translate directly from a speech in one language to text in another. For example, automated transcription could translate a doctor’s conversation with a patient precisely into a transcribed text document when applied to EHR.
Virtual clinical trials present opportunities to enhance the quality and effectiveness of clinical trials, enlarging their scale and reach, decreasing the patient’s burden, improving engagement, and streamlining clinical trial workflows. Wearables and smartphones play a critical role in accelerating the clinical trial processes, collecting vital information for a study outside the clinical setting, and transmitting it to the doctors’ analysis.
Moreover, patients were ready to use various digital health technologies like wearables, ingestible sensors, and mobile phones if they were practical and easy to use. Mobile technologies improve patient satisfaction and engagement, recruitment, and clinical trial feasibility.
Mobile technology brings solid value to clinical research, as it brings up the continuous real-time collection of previously inaccessible mobile data, making mobile technology exciting and worthwhile. If you’d like to virtualize your clinical trial using mobile technology, contact us using the contact form at the bottom of this page.
Boosting pharmaceutical manufacturing and distribution is a challenging process with many elements and moving parts involved. To show how digital transformation can redefine the these processes, there are several key aspects to consider. Let us take a closer look at them.
How to connect everything with everything within the life sciences? Internet of Things (IoT).
Pharmaceutical production lines connect with suppliers, and relevant medicines connect with customers, sales reps connect with health professionals, and all in near-real time.
IoT is disrupting the traditional distribution value chain, forcing pharmaceutical manufacturing companies to rethink and re-supply the conventional pharmaceutical manufacturing and distribution processes. IoT enables strategies like mass customization to be more cost-effective. With a real-time information exchange between buyers and manufacturers, pharma customer representatives can see what drugs are being ordered and enable the system to re-supply the manufacturing chain on the fly.
IoT offers dramatic reductions in costs for asset management. In particular, IoT has helped to achieve a 6.8% improvement in production throughput due to asset tagging and a 10 to 25 increase in build-to-order cycle times (18 months reduced to two weeks).
For example, imagine if the pharmaceutical customer representatives learned that doctors were prescribing the medications the representatives were advertising to treat new health conditions that weren’t originally thought over by the marketing team. The opportunities are endless. For instance, drug discovery teams could work on remedies patients need the most; customer representatives could sell those drugs to doctors to treat anticipated health conditions; and manufacturing support teams would be alerted to the potential problems before the machines could break. No magic at this point, just a pure digitally connected pharma manufacturing world. To name another example, remember when cloud computing appeared? While it seemed futuristic initially, we can’t live without working in the cloud anymore, as we store our data, edit documents, and back up our photos.
Instant information exchange empowers pharmaceutical manufacturers to ensure the highest-demanded drugs are being manufactured when the patients need them the most. For example, during the COVID-19 pandemic, a few drugs (hydroxychloroquine and chloroquine) tested as possible remedies to the coronavirus were officially experiencing shortages.
Pharma manufacturing units are running at a greater capacity than ever, facing a 24/7 operation throughput. Yet, at this critical moment, manufacturers don’t have the time for emergency equipment maintenance, and we’re not discussing scheduled services. With downtime costing a unit up to $20,000 a minute, a manufacturer can not afford a disruption in the production processes.
Now that IoT sensors can be embedded across the pharmaceutical manufacturing unit and predictive analytics solutions are paving the way into the sector, it’s another chance for pharmaceutical manufacturers to use tech for robotic process automation and optimization.
Murphy’s Fourth Law states, “If there is a possibility of several things going wrong, the one that will cause the most damage will be the one to go wrong.” Equipment and machines can break down, which often happens in the most inopportune times and awkward places.
Real-time data captured from sensors installed across pharmaceutical manufacturing units can be utilized for predictive analytics and maintenance. Some multiple predictive solutions and algorithms can be implemented for pharma units. The ultimate goal of predictive algorithms is to correlate the impact of numerous variables, including temperature and drug component proportions, along with predicting the trends with statistical precision. Predictive analytics solutions can alert that there’s a certain percent of probability that problem X will happen in a specific period, which is enough to alert responsible people to implement preventive measures. Some predictive maintenance solutions can go even further, alerting when your firm needs to fix a problem before the likely new failure occurs.
No one talks about quality anymore. It’s all about managing quality and doing it right. Quality management is paramount at every clinical research stage, from drug discovery to drug safety and distribution. Inconsistent quality assurance in the pharmaceutical manufacturing phase can lead to unnecessary recalls or treatment shortages, resulting in unexpected losses for sponsor companies. Furthermore, many new challenges and complexities not evident during drug research and development may appear when drug manufacturing is scaled to commercial production. Those are the most vivid examples of failed quality management practices.
Data analytic tools support the effectiveness of pharmaceutical manufacturing processes, enabling real-time monitoring of critical variables on the production line. Case in point, wireless temperature sensors can help monitor the medication’s temperature during production batches. In addition, vacuum sensors aid with continuous pressure audits to ensure no zero drifts and no deviations in pressure for products that require complete vacuum accuracy.
These two advanced data analytics approaches are just a few illustrations of digital tools optimizing the manufacturing process, workflow efficiency, and reducing quality issues. Moreover, data analytics can improve quality at the drug discovery stage. The data collected from medical devices provides insights into chemical formulations, fermentation, and crystallization processes and enables pharmaceutical professionals to make swift data-driven decisions to refine every subsequent experiment.
Pharmaceutical companies and other industries harnessing the benefits, competitive advantage, and advantages of innovative developments in the digital age are already improving their quality assurance processes across every stage, reducing their expenditures, ensuring confidence in purchased drugs, and bringing in trust.
Drug commercialization can transform drug development efforts into profitable pharmaceutical products sold across continents. With the help of digital technologies, such as CRM, multi-channel marketing automation, intelligent scheduling, and document management, the spent drug commercialization efforts can be translated into a multiplied ROI. In addition, digital tools help to foresee tendencies in the pharma industry and provide actionable data that brings in transformative business decisions.
A Customer Relationship Management (CRM) platform can fast-track drug development and commercialization processes. Salesforce provides a variety of CRM categories and systems for pharmaceutical needs. In particular, the Salesforce Marketing Cloud can help contract research organizations (CROs) to enroll patients faster. Furthermore, integrating the legacy systems within the Salesforce Marketing Cloud allows for all-inclusive marketing intelligence and refined customer engagement.
Salesforce CRM is a very flexible system with workflow and parameters that can be fully tailored to the client’s needs. As a result, the system can provide actionable insights into every phase of clinical trial management, empowering clients and key stakeholders to connect and use data within their studies in an entirely new way.
An orchestrated customer management solution may include a CRM interconnected with a planner, appointment scheduler, VoIP, and other digital technology solutions that allow pharmaceutical customer representatives to better organize their communications with doctors. In such a system, medical representatives can easily track the sales of different pharmaceutical products and easily communicate with sponsor companies via web solutions and by contributing to the system using mobile iOS and Android apps while working in the field.
Once CRM is configured and tailored to the life sciences workflows, it can aid with displaying project risks, presenting actual study progress, alerting research teams of critical milestones, and being a platform for collaboration and exploration.
Pharmaceutical marketing activities define how successful a drug will be in the market. A multi-channel marketing automation system helps pharmaceutical customer representatives to interact with physicians using different strategies and channels: online and offline meetings, ads, and emails. In addition, the predictive capabilities of such a system will remind customer representatives when they need to schedule appointments, with which doctor, and in which location, accelerating the overall pharmaceutical marketing efforts.
A multi-channel marketing automation system consolidates most pharma companies. Another industry is’ marketing workflows and mobile communications into one system, allowing anyone to understand and see the complete picture of all the efforts expended. The alerts and intelligent notifications ensure no gaps in marketing communication plans. For example, Pharma customer representatives can easily segment the doctors they target depending on their interests, create interactive emails, and resend unopened ones.
When it comes to the final step of drug development, namely pharmacovigilance, digital transformation can be a game-changer. Here are some crucial examples of how that can pan out.
Adverse event reporting is highly fragmented on a global level. The adverse event data is saved in various formats across different databases, including text, images, and video. In addition, pharmaceutical organizations have to keep pace with multiple pharmacovigilance regulations, laws, directives, and guidance both on a global and national level, some of which may contradict or overlap with each other. There’s no standardized scenario or schema to consolidate all the adverse event data, as every organization operates under its silos.
Inaccurate adverse event classification in clinical trials may have serious consequences, including data bias. Therefore, it’s paramount to standardize adverse reactions at every step of the journey, from patient reporting to the presentation of adverse responses in publications. Adverse event misclassification may lead to bias. For example, the MedDRA dictionary is updated annually with new codes belonging to different subgroups. This increase in categories makes it harder to detect adverse drug reactions and has the potential to compromise patient safety.
The pressure to shrink costs and minimize the number of people involved in making C-level management rethink their traditional pharmacovigilance approach. As a result, Pharma companies are reorganizing their regular pharmacovigilance systems and utilizing the power of new techs, such as data science and ML, to handle previously manual and routine tasks. Such decisions often result in smarter decisions that are not necessarily a higher cost for pharmacovigilance tasks.
ML and NLP technologies help to transform the entire pharmacovigilance workflow. They reduce the time and costs spent on routine pharmacovigilance tasks. Intelligent automation, advanced analytics, and reporting within pharmacovigilance platforms help aggregate data from different sources and channels and thoroughly analyze any side effects data to determine essential insights.
ML, and in particular, it is deep subfield learning, can quite accurately predict adverse drug reactions. In addition, robust statistical methods, such as Cosine distance, can aid the correct elicitation and validation of adverse drug events from the available data. Statistical similarity methods also help to exclude noise-induced adverse drug events while maintaining at the same time an advanced level of correlation precision.
Electronic health records (EHR) are becoming omnipresent. EHR of big medical institutions can incorporate data from over 10 million patients over 15 years. The data in EHR can represent 200,000 years of physician’s wisdom and 100 million years of patient outcomes data, including many adverse event reports and rare conditions. Consequently, the need to apply deep learning and NLP algorithms to EHR is growing exponentially.
In particular, recurrent neural networks (RNNs) are efficient deep learning algorithms that can process text, speech, and time-series data. The deep learning system processes EHRs in two steps. First, it accumulates the raw EHR data, and second, it maps and parses the data into a standardized format that can be used across health systems. After such standardization, researchers can infer to the AI to answer the following questions: ‘Which elements of a patient’s medical history should be evaluated?’, ‘How do they map to the patient’s current health condition?’ and ‘What options can be resolved?’ .
Pharmaceutical organizations often receive similar data from different sources, and this data does not always correspond to each other.
This is particularly vivid with adverse event data, which needs to be more cohesive, misclassified, and unstructured. Data governance is a process that helps with pharmaceutical data validation and ensures all the records are accurate and errorless. To tackle data governance issues, life science firms must set up special departments that oversee data governance and validation. These departments are responsible for well-thought-out data management strategies and ensure data validation policies and procedures are carried out. Moreover, pharmaceutical companies are turning their heads towards data management solutions that simplify data validation procedures and help to achieve data governance goals.
Uniting together software development and our regulatory consulting expertise, we can assist our customers in addressing pharmaceutical regulations requirements and providers’ expectations and anticipate patients’ needs. Our strong consulting team can help you define and smoothly sail through regulatory and software development hurdles to reach your patients more quickly. This allows you to easily transition your breakthroughs from the laboratories to the patients that need the medication the most.
With technology and regulatory consulting for pharma organizations, we empower our clients to accelerate their timeliness, improve conventional workflows and decrease the risk of postponement.
The next normal has already arrived. It is shaped by a range of factors and new global experiences that spark waves of innovation. Examples are the tremendous growth of digitization, from IoT for remote patient monitoring to evolutionary algorithms for drug development, that has changed the pharma industry landscape substantially. Accelerated digital transformation boosts medical innovation, and with tech working in combo with biology, it is pervading deeper into all the phases of the drug development life cycle.
For sponsor companies, the real benefit from technology and digitization lies in the innovation and differentiation of healthcare products. Digital technology enables adopting solutions and digital capabilities into processes and workflows that weren’t considered possible. The development of the COVID-19 vaccine is the most vivid example of the digital success of incorporating technology and digital tools into delivery within a new environment.
Achieving the long-term impact of digital transformations requires thoughtful and conscientious planning, setup, configuration, integration, and deployment of technological software solutions and instruments. Digital technologies in the pharma industry are maturing quickly, offering benefits for companies and patients. Knowledge graphs, NLP, neural networks, evolutionary algorithms, advanced analytics, automation, ML and AI, bioengineering, and genetic sequencing combined with well-known and proven tools are the critical elements of success in the market.
As always, there are limitations to overcome and trade-offs to regulate. The dynamics of competition in the pharma industry are changing, with smaller players raising the bar of competition as advanced technological capabilities are quickly adopted. The shift of responsibility and management in the pharmatech model ends with the transition of technical complexities to outsourcing providers.
Contact us, and our biotech and pharmaceutical customers are already benefiting from a less steep digitization curve. As a result, they can tap into innovation and leverage the power of technology to serve their patients better. Join a path to digital transformation with us.
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