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What you will learn
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?
- Predict the behavior of chemical compounds in potential drugs and their pharmaceutical ADME (absorption, distribution, metabolism, and excretion) properties using neural networks for drug discovery.
- Identify molecules that are synthesizable, stable and refined for multiple criteria with the help of genetic programming and evolutionary algorithms.
- Collect, consolidate, structure and extract valuable entities from unstructured data. Find medications that can treat COVID-19 using knowledge graphs.
- Determine prospective target molecules from millions of candidate compounds. Improve the outcomes and quality of the compound analysis using high-throughput screening.
- Store data from virtual compound screening on cloud services, while scaling up and down to address the computational demands.
- Identify influential principal investigators who can empower clinical trials by recruiting eligible patients using natural language processing (NLP) for patient enrollment.
- Predict patient outcomes from electronic health records (EHRs) using NLP, machine learning and recurrent neural networks.
- Obtain data-driven definitions of diseases using computational disease phenotyping.
- Revolutionize real-time monitoring of diseases by collecting essential data, like heart rate, glucose levels, movement disorders, concussions and other medical events using wearables and IoT for remote patient monitoring.
- Transcribe patient visits using voice assistants for ultimate precision.
- Enhance the quality and efficacy of clinical trials, enlarge the scale and reach, decrease the patient’s burden, and improve engagement with virtual and decentralized clinical trial implementation.
- Reenergize a traditional distribution value chain, enabling mass customization and a real-time information exchange between buyers and manufacturers using IoT for drug asset management.
- Anticipate the possible breakdown of machines and equipment using predictive maintenance systems for pharma manufacturing.
- Ensure the pharmaceutical production line has no defects and that customers enjoy seamless treatments with quality assurance practices.
- Better organize the collaboration between pharmaceutical customer representatives and doctors using Salesforce CRM.
- Consolidate marketing workflows into unified multi-channel marketing automation systems and ensure there are no gaps in marketing and sales activities.
- Exclude noise-induced adverse drug events as well as aggregate and systematize adverse event reports from different sources and channels using deep learning techniques.
Implement a well-thought out data management strategy and ensure elaborate data validation policies with data governance.
This guide 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:
- Drug discovery
- Clinical trials
- Pharmaceutical marketing and distribution
- Drug commercialization
Technologies revolutionizing pharma
Since the very start of human civilization, people have been looking for substances that can 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 technology 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 the previously routine and manual tasks, and considerably speeding up drug discovery processes.
This article highlights how the advances in digital technology have accelerated drug discovery, streamlined clinical trials, eased up drug commercialization processes and improved drug safety.
1. Augmenting drug discovery
As per Eroom’s law, the cost to develop a new drug roughly doubles every nine years. The most time-consuming and costly phase of developing new drugs is drug discovery. The drug discovery phase is one of the major undertakings for pharmaceutical organizations with just one third (31.8%) of all pre-clinical studies actually entering Phase 1 of a clinical trial. A vast majority of drug discovery studies are unsuccessful. The full cycle of drug development may last 10 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 even 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 small number of molecules get through to the succeeding stages.
A variety of digital technologies and approaches are employed in the search for the right substances that have the desirable therapeutic effects. Among them are neural networks and knowledge graphs for drug discovery, high-throughput screening (HTS), evolutionary algorithms and genetic programming.
Neural networks for drug discovery
After a period of not much progress, the field of artificial intelligence (AI) has experienced a boom in its capabilities. The shift happened when scientists reproduced the way 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, pharmaceutical professionals can benefit from the mass of unstructured medical data.
Historically, developing a machine learning (ML) algorithm required extensive software engineering expertise in order to develop feature extractors which 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 its own representations needed for pattern recognition. Deep learning algorithms are capable of processing highly complex functions, can be scaled to big datasets, and are able to continue to improve 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. By the same token, machine learning helps to decrease drug failure rates during clinical development stages which makes it a valuable and cost-saving tool.
Neural networks are proving to be the most powerful and valuable methods in the field of deep learning. They are effective 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 are able to:
- Predict pharmaceutical ADME (absorption, distribution, metabolism, and excretion) properties of molecular compounds and targets for drug discovery.
- Predict how chemical compounds will behave in the potential drugs.
The neural network classifiers are capable of allocating not only existing chemicals, but also generalizing related areas of chemical spaces to virtual chemicals. As an example, inexistent chemical compounds can be manufactured with neural network predictions determining what chemicals will be applicable in a drug.
Genetic programming and evolutionary algorithms for drug development
Biopharma professionals are going a long way to identify and optimize the lead molecular compounds which are able to bind to the proteins that cause a disease. Genetic programming is a useful method for the instant identification of the necessary chemical compounds for a pharmaceutical drug discovery. The algorithms are capable of generating new ideas for molecules that can be used in treatments. In this way researchers can shorten up 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 easily identify molecules that are synthesizable, stable, and refined for multiple criteria. What’s more, 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 on be consolidated into massive-scale compound libraries that can be utilized to speed up the preclinical discovery of needed compounds.
Pharma companies are using genetic programming to analyze the molecules with a mission to shorten the amount of time spent on the drug development process. Molecule representations based on quantum chemical calculations enable the examination of the compound properties in order to better understand how the molecules may act in a body.
Knowledge graphs for compound identification
What SARS-CoV-2 triggered is the acute necessity of a quick identification of any critical compounds that can halt the global distribution of deadly viruses, now and in the future. Data, extracted from scientific literature in the structured medical repositories, becomes a real instrument for the identification of approved medications that can inhibit a respiratory infection.
Open and proprietary biomedical data sources allow for collecting, consolidating, structuring and extracting valuable information when it’s needed the most. 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 the 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 overreaction of the immune system). By analyzing data via the knowledge graph, the prospective medications that can halt the ability of the virus to infect the lungs were identified.
Pharmaceutical companies evaluate the capabilities of experienced tech services firms to extract valuable insights from unstructured medical data and represent this information in a knowledge graph.
“Rather than expecting us to tell Avenga what to do, their team is part of the thought process. The team is extremely thorough, delivers the highest quality results, and adheres to deadlines. They are a committed partner that provides proactive insights. The collaboration is efficient and undemanding”.
Group VP & Process Excellence Manager, ABB
→ Read how the NLP social graph technique helps to assess patient databases and can help clinical research organizations succeed with a clinical trial analysis.
Virtual compound screening
In the past, the compound screening process was quite inefficient as it involved empirical observations and random screening. At present, the compound screening process has considerably improved with high-throughput screening (HTS) technology. HTS allows for a 1,000 times faster compound screening that produces over 100 million reactions in less that 10 hours, with just 1-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 much 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 full data in the shortest time periods. The important 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. AI helps to glean biochemical significance from the huge amount of data that is generated by HTS. In particular, machine learning algorithms, such as quantitative structure-activity relationship (QSAR) modeling, can help to determine prospective target molecules from the millions of candidate compounds.
Cloud computing for pharmaceutical workflows
Cloud computing has already changed the way we do everyday tasks. It’s disrupting businesses all over the world, including biotech and pharma organizations, by creating new ways to operate and integrate different workflows and tools.
For example, cloud-based computing platforms already assist researchers conducting virtual compound screenings and high-throughput molecular dynamics. A virtual compound screening may take weeks or months to conduct using 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 easily scale up and down to address the computational demands. 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 are enabling 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 from any device in the world. Furthermore, cloud vendors provide simplified backup/recovery and load balancing options; the cloud vendor’s global infrastructure consists of isolated zones with a low network latency.
Avenga experts are certified in all three major public cloud providers (AWS, Azure, GCP). We are not biased towards any vendor or cloud in general and we can offer a fair comparison between vendor-specific, vendor-agnostic, and hybrid options. Additionally, we are not a cloud-only consultancy, so we can offer a more in-depth analysis by leveraging our expertise in other technology areas as well.
2. Empowering clinical trials
Natural language processing for effective patient enrollment
As trite as it sounds, COVID-19 has evidenced the urgency for effective treatments. In order to deliver efficacy of medications, and do it quickly (another ambiguity), the companies conducting clinical trials strive to engage a greater number of patients in order to reach the 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, without a sufficient number of clinical trial participants the study has lower statistical power and an insufficient study validity, which may bring negative consequences at a later stage.
To address the issue of low patient enrollment, an effective 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 the sufficient numbers of patients for a clinical trial.
NLP is focused on processing texts and natural language to infer meaning from the words and texts. NLP methods help to transform raw medical data and enrich it so as to deliver real 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 that have researched specific topics. On social graphs, pharmaceutical organizations can quickly see which doctors are already working as investigators in a particular clinical trial and which ones can potentially be invited to participate in a trial.
QPharma, a US-based life sciences company, was able to increase its patient engagement rate by using NLP technology from Avenga to identify investigator-influencers.
If you’re looking to develop software and data science powered solutions for your clinical organization, you are welcome to contact us.
Predicting patient outcomes from electronic health records (EHRs)
Over the last 10 years, the adoption of EHRs across healthcare facilities has skyrocketed, partly because HITECH legislation came into force. 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 different medical codes reflect various aspects of patient encounters, such as laboratory tests, diseases, medications, clinical procedures. Originally these codes were implemented for administrative and billing tasks only, however, they contain important information for secondary pre-processing. The latest deep learning approaches are being used to project distinct medical codes at a vector pace for detailed data analysis and predictive tasks.
Machine learning algorithms can better leverage information-rich raw data in EHRs. For example, clinical notes are frequently overlooked when developing predictive systems. 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 such compilations.
Large-scale recurrent neural networks (RNNs) are demonstrating remarkable predictive results by combining unstructured and structured data for semi-supervised learning. Neural networks are quite successful in:
- Extracting disease names, procedures, and treatments used in EHRs and unstructured clinical notes.
- Associating medical events and their corresponding time spans or date from clinical notes, such as in the past 3 months.
- Extracting relationships such as ‘pharmaceutical product X improves/worsens the condition Y.’
- Medical abbreviation expansion with word embeddings that achieve a 82% accuracy compared to a baseline that equals 20-30%.
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:
- Static patient outcome prediction; e.g. predicting heart failure, identifying bone disease risk factors, classifying diagnosis, or predicting hypertension.
- Temporal patient outcome prediction; e.g. predicting the outcome within a set time interval or making a prediction on the basis of time series data, like predicting an unplanned readmission following a hospital discharge.
If you’d like to utilize neural networks and machine learning to predict patient outcomes, you are welcome to contact Avenga.
Learn how natural language processing can be used to understand an unstructured text and then parse the information from it. The approach we apply to extracting relevant entities makes it possible to group documents and find similarities according to a required set of features.
Computational disease phenotyping for precision medicine development
As the digitized EHRs resulted in huge amounts of medical data, new opportunities emerged to refine and review diagnosis definitions and boundaries. Considering that diseases are traditionally characterized by a set of manual clinical descriptions, computational disease phenotyping aims to obtain data-driven definitions of illnesses. Machine learning and data mining techniques are able to detect more fine-grained illness descriptions. Computational disease phenotyping is a huge step forward towards precision medicine and personalized healthcare.
Computational phenotyping enables “the data to speak for itself” by detecting relationships and concepts from unstructured medical data without any supervision or bias.
→ Explore how software development for clinical trials can equip and complement biotech and pharma companies that are seeking out facilities to run their clinical trials with the utmost efficiency, and to move new treatments to the market faster than the competition.
Wearables for remote patient monitoring
Wearables opened up a whole new world of opportunities for pharma and life sciences. They enable 80% faster decision-making, thanks to the workforce enablement, which revolutionized the real-time monitoring of diseases by collecting essential data, such as heart rate, glucose levels, movement disorders, concussions and other medical events.
→ Discover 20 examples of wearables and IoT disrupting healthcare
Wearables help to decrease healthcare costs by reducing the number of in-person visits to the clinic. The health data that is collected by a simple medical wearable device can be life-saving. IoT devices can be used to potentially intervene in certain circumstances. In addition, combining the mounds of microscopic edible sensors ingested in our bodies and the ones that we wear on our body is transforming diagnostic and preventive care as we know it now.
There are numerous success cases of how wearables are reshaping healthcare. For example, a combination of cloud software and wearable devices can monitor patients’ vital signals and send alerts to medical personnel about potential accidents or falls. This system proved to be so effective in a facility that serves eldery patients, that now even the patients’ relatives can remotely monitor the well-being of their family members.
To utilize the full potential of wearable technology, life sciences organizations may utilize the help of experienced product development companies, like Avenga.
“Avenga provided a complete mobile team to develop a cost-effective health care sourcing app, augmenting internal staff in creating a modifiable solution while adapting to evolving requirements”.
Former CTO, M3Health Tech
Doctors can spend 6 hours in an 11-hour workday engaged in the electronic health records (EHRs) documentation process, which can cause exhaustion and also shortens the time spent with patients. The next generation 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, the language translation, based on recurrent neural networks, can translate directly from the speech in one language to a text in another language. When applied to EHR, automated transcription could translate a doctor’s conversation with a patient precisely into a transcribed text document.
Virtual and decentralized clinical trial implementation
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 the vital information for a study outside the clinic setting, and transmitting it to the doctors’ analysis. Remarkably, 81% of patients who took part in the Contemporary Clinical Trials Communications survey, held in July 2019, were willing to participate in a mobile clinical trial while just 51% of patients were willing to take part in traditional clinical trials.
Moreover, patients showed a readiness to use a variety of digital technologies like wearables, ingestible sensors and mobile phones, if they were practical and easy to use. Mobile technologies improve patient satisfaction and engagement, as well as recruitment and clinical trial feasibility.
Mobile technology is bringing solid value to clinical research, as it brings up the continuous real time collection of previously inaccessible mobile data, which makes mobile technology exciting and worthwhile. If you’d like to virtualize your clinical trial using mobile technology, get in touch with us using the contact form at the bottom of this page.
3. Improving pharmaceutical manufacturing and distribution
IoT for drug asset management
How to connect everything with everything within the life sciences? IoT. Just like that.
Pharmaceutical production lines connecting with suppliers, relevant medicines connecting with customers, sales reps connecting 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 at the beginning, we can’t live without working in the cloud anymore, as we store our data there, edit documents there, and backup our photos there.
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) which were tested as possible remedies to the coronavirus were officially experiencing shortages.
Pharma manufacturing units are running at a greater capacity than ever before, facing a 24/7 operation throughput. At this critical moment, manufacturers don’t have the time for emergency equipment maintenance, and we’re not talking about scheduled services. With a 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 make use of tech for process optimization.
→ Explore how data obtained from IoT devices can help life science companies make better decisions while gaining a competitive advantage.
Predictive maintenance for pharma manufacturing
As per Murphy’s Fourth Law, “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, and often this happens at the most inopportune times and in the most awkward places.
Real-time data captured from sensors installed across pharmaceutical manufacturing units can be utilized for predictive analytics and maintenance. There are multiple predictive solutions and algorithms that can be implemented for pharma units. The ultimate goal of predictive algorithms is to correlate the impact of multiple 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 time period, which is enough to alert the 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.
Quality assurance in production lines
No one talks about quality any more. It’s all about managing quality and doing it right. Quality management is paramount at every stage of clinical research, starting at drug discovery all the way to drug safety and distribution. Inconsistent quality assurance in the pharmaceutical manufacturing phase can lead to unnecessary recalls or treatment shortages, and result in unexpected losses for sponsor companies. Furthermore, new complexities, that haven’t been evident during drug development, may appear when the 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 with monitoring the medication’s temperature during production batches. Vacuum sensors aid with continuous pressure audits to ensure there’s no zero-drifts and no deviations in pressure for products that require a full vacuum accuracy.
These are just a few illustrations of digital tools optimizing the overall manufacturing workflows efficiency and cutting down the quality issues. What is more, data analytics can be used to improve quality at the drug discovery stage. The data collected from medical devices provides insights into chemical’s formulations, fermentation and crystallization processes, and enables pharmaceutical professionals to make swift data-driven decisions to refine every subsequent experiment.
Pharmaceutical manufacturing companies that are harnessing the benefits of innovative developments are already improving their quality assurance processes across every stage, reducing their expenditures, ensuring confidence in purchased drugs, and bringing in trust. Experienced IT services firms, like Avenga, can provide you with a full spectrum of quality assurance and testing solutions as per your needs, all while appreciating the quality of your product.
“I am confident regarding the quality of their services – quality measures in terms of technical, soft and linguistic skills are fulfilled, which significantly contributes to the success of our projects.”
“The overall experience, positions the company as a highly-recommended business partner on the international level.”
4. Igniting drug commercialization
Drug commercialization can transform drug development efforts into a profitable pharmaceutical product sold across different continents. With the help of digital technology, such as CRM, multi-channel marketing automation, intelligent scheduling, and document management the spent drug commercialization efforts can be translated into a multiplied ROI. Digital tools help to foresee tendencies in the pharmaceutical industry and provide actionable data that brings in transformative business decisions.
New ways to utilize Salesforce CRM
A Customer Relationship Management (CRM) platform can fast track not only drug development, but also the drug 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, the integration of the legacy systems within the Salesforce Marketing Cloud allows for all-inclusive marketing intelligence and a refined customer engagement.
Salesforce CRM is a very flexible system with workflow and parameters that can be fully tailored to the client’s needs. For instance, one of Avenga’s clients has implemented a highly configurable Salesforce-based suit (mobile, web and app) providing real-time access for key clinical trial needs. The system is capable of providing 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 technological 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, not only via web solutions, but also by contributing into 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 key milestones, and being a platform for collaboration and exploration.
→ As the Top Salesforce Consulting Company, per Clutch’s rating, and as an official Salesforce Partner, Avenga helps clients translate a Salesforce investment into a quality deployment and timely project implementation. Clients trust us for a valuable experience across a variety of Salesforce development services, Salesforce solutions and products.
Multi-channel marketing automation
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, emails, etc.. Predictive capabilities of such a system will remind customer representatives when they need to schedule meetings, with which doctor, and in which location, accelerating the overall pharmaceutical marketing efforts.
A multi-channel marketing automation system consolidates all pharma marketing workflows into one system, allowing anyone to understand and see the full picture of all the efforts expended. The alerts and smart notifications ensure there’s no gaps in marketing communication plans. Pharma customer representatives can easily segment the doctors they target depending on their particular interests, create interactive emails, and resend unopened ones.
→ Sales processes, together with pharmaceutical data governance, data analytics, and report creation, can benefit from pharma sales analytics and reporting and deliver results instantly.
5. Simplifying pharmacovigilance
Deep learning for adverse event monitoring
Adverse event reporting is highly fragmented on a global level. The adverse event data is saved in a variety of formats including text, images, and video, across different databases. 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 own silos.
Inaccurate adverse event classification in clinical trials may have serious consequences, including data bias. It’s of paramount importance to standardize adverse reactions at every step of the journey, starting from patient reporting to the presentation of adverse reactions in publications. Adverse event misclassification may lead to bias. For example, the MedDRA dictionary is annually updated with new codes that belong 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 is making C-level management rethink their traditional pharmacovigilance approach. Pharma companies are reorganizing their regular pharmacovigilance systems and utilizing the power of new-tech, such as data science and machine learning, to handle previously manual and routine tasks. Such decisions often result in smarter decisions that are not necessarily a higher cost for pharmacovigilance tasks.
The machine learning (ML) and natural language processing (NLP) technologies help to transform the entire pharmacovigilance workflow. They reduce time and costs spent on routine pharmacovigilance tasks. Intelligent automation, data analytics, and reporting within pharmacovigilance platforms help to aggregate data from different sources and channels, and conduct a thorough analysis of any side effects data to determine important insights.
Machine learning, and in particular its subfield deep learning, has the capabilities to quite accurately predict adverse drug reactions. Robust statistical methods, such as Cosine distance, can aid with 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.
Natural language processing and neural networks for electronic health records
Electronic health records (EHR) are becoming omnipresent. EHR of big medical institutions can incorporate data from over 10 million patients over 15 years. In total, the data in EHR can represent 200,000 years worth of physician’s wisdom and 100 million years of patient outcomes data, including a large amount of adverse event reports and rare conditions. Consequently, a need to apply deep learning and NLP algorithms to EHR is growing exponentially.
In particular, recurrent neural networks (RNN) 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 secondly, 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 are the options that can be resolved?’
In this way data scientists, in companies like Avenga, are beginning to employ unsupervised learning approaches to make diagnosis predictions by utilizing auto-encoders that train neural networks to learn from relevant representations and then reconstruct unlabeled data.
→ How can businesses leverage NLP? What are the main areas of natural language processing applications? Having first-hand experience in utilizing NLP for the healthcare field, Avenga can share its insight on the topic.
Data governance for pharmacovigilance
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 is highly fragmented, misclassified and unstructured. Data governance is a process that helps with pharmaceutical data validation and ensures all the records are accurate and errorless. In order to tackle data governance issues, life science firms have to set up special departments that oversee data governance and data validation. These departments are responsible for well-thought out data management strategies and ensure that data validation policies and procedures are carried out. Moreover, pharmaceutical organizations are turning their heads towards data management solutions that simplify data validation procedures and help to achieve data governance goals.
→ How does strategic management of data assets work for business in reality? In a concise overview, we explain what Data Governance is and why it matters to business. Explore the howS and whyS of the Data Governance strategy alongside the key principles and components of the Data Governance framework, and how it influences decision-making.
6. Digital technology consulting for pharma companies
Digital technology is one of the most powerful weapons that can be used to change the world. If you’re looking for a digital transformation that can be executed at scale, our pre-eminent pharmaceutical industry experts can help you reinvent your legacy solutions and drive continuous growth.
Reasons why you should consider partnering with Avenga:
- Having 20 years of experience as a technology partner, we bridge together domain knowledge and capabilities, enabling our pharmaceutical clients to get technologically fit and stay ahead of the competition.
- Being a truly global consultancy (not just sales guys around the globe like many competitors have); with delivery centers in Germany, Poland, Ukraine, and Malaysia along with a sales office in the United States. Our optimal blend of delivery locations allow us to make unique proposals in terms of price, offered value, proximity and compliance.
- Operating in our in-house R&D center, we work tirelessly to produce effective technological solutions and viable results for our clients.
- Providing rapid scalability, thanks to efficient recruitment processes. We can extend your team with the right tech talent either for short term or long term, in addition to upscaling or downscaling your team whenever needed.
- Offering flexibility via various engagement models, such as time and material, fixed prices, and dedicated teams.
- We are a Salesforce Silver partner, Microsoft Gold partner, Xamarin premier consulting partner, and one of the Top 100 Global Outsourcing companies as selected by IAOP and Fortune.
7. Regulatory consulting for life sciences organizations
Uniting together software development and our regulatory consulting expertise, we can assist our customers to address pharmaceutical regulations requirements, providers’ expectations and anticipate patients’ needs. Our strong consulting team can help you clearly define and smoothly sail through regulatory and software development hurdles in order to reach your patients quicker. This allows you to easily transition your breakthroughs from the laboratories to the patients that need the medication the most.
We provide regulatory consulting services to help pharmaceutical organizations all over the world to incorporate scientific discoveries into newly released drugs. From drug development to drug commercialization, our knowledge and expertise is reinforced with a deep belief in what we do.
“Affordability and depth of expertise have made them a critical development partner. Their team easily scales to accommodate project size and is equally flexible with scheduling across time zones”.
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.
On the path to the future
The next normal has already arrived. It is shaped by a whole range of factors and new global experiences that are sparking waves of innovation. Examples are a tremendous growth of digitization, from IoT for remote patient monitoring to evolutionary algorithms for drug development, that has changed the Pharma landscape substantially. Digital transformation accelerates 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 innovation and differentiation. Digital technology enables the adoption of solutions and workflows that weren’t thought to be possible before. The development of the COVID-19 vaccine is the most vivid example of incorporating technology and digital tools into delivery within a new environment.
Achieving the long term impact of digital transformation requires thoughtful and conscientious planning, setup, configuration, integration and deployment of the technological software solutions and instruments. The digital technologies in the pharmaceutical industry are maturing quickly offering benefits not only for pharma companies, but for patients alike. Knowledge graphs, NLP, neural networks, evolutionary algorithms, data analytics, automation, machine learning and AI, bioengineering, and genetic sequencing combined with well-known and proven tools are proving to be the key elements of success in the market.
As always, there are limitations to overcome and trade-offs to regulate. The dynamics of competition in the pharmaceutical 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 like Avenga. Our biotech and pharmaceutical customers are already benefiting from a less steep digitization curve and are able to tap into innovation and leverage the power of technology to better serve its patients. Join a path to digital transformation with us.
Confidently navigate the complex landscape of pharma and life sciences with the knowledge of these critical pharma trends.
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