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In 2020, despite several challenges due to the COVID-19 pandemic, the FDA has approved many novel products that served previously unmet medical needs and significantly helped in advancing patient’s quality of life. The broad indication wise distribution (Figure 1) of all CDER’s 2020 drug approvals indicates notable advances in drug discovery1,2.

Figure 1: Indication wise drug approvals (BLA vs NMEs)

Figure 2: Proportion of NDA vs BLAs in new drug approvals

CDER, approved 53 novel drugs*, either as New Molecular Entities (NMEs) under New Drug Applications (NDAs: 74%) or as new therapeutic biologics under Biologics License Applications (BLAs: 26%).

New Drug Approvals (FDA) in 2020

Table 1. The list of 53 approved drugs:

Significant drug launches of 2020

Many of the novel entities approved in 2020 are notable for their potential positive impact and unique contributions towards medical care.

First-in-class novel drugs

40% of novel drugs (21 of 53) approved as ‘First-in-class’. Few notable approvals include:

  • Rukobia (Fostemsavir, Viiv Healthcare, 07/02/2020)
    A new type of antiretroviral medication to treat HIV-1 via gp120:CD4 cellular interaction.
  • Koselugo (Selumetinib, AstraZeneca LP, 04/10/2020)
    MEK1/2 (RAF-MEK-ERK) inhibitor, for treatment of certain pediatric patients with neurofibromatosis type 1 (NF1PN).

Orphan novel drugs

58% of novel drugs (31 of 53) designated as ‘Orphan status’ to treat rare diseases. Notable examples with rare diseases include:

  • Evrysdi (Risdiplam, Genentech Inc, 08/07/2020)
    mRNA splicing modifier for SMN2, used as a treatment for spinal muscular atrophy (SMA).
  • Lampit (Nifurtimox, Bayer Healthcare Pharms, 08/06/2020)
    The first therapy approved by FDA to treat pediatric patients with chagas disease.
  • Orladeyo (Berotralstat, BioCryst Pharmaceuticals Inc, 12/03/2020)
    A plasma kallikrein inhibitor, to treat patients with hereditary angioedema (HAE).

Other notable drug approvals

  • Artesunate (Amivas LLC, 05/26/2020)
    It helps in the treatment of severe malaria in adult and pediatric patients by inhibiting EXP1, a glutathione S-transferase.
  • Imcivree (Setmelanotide, Rhythm Pharmaceuticals Inc, 11/25/2020)
    It is a MC4 receptor agonist for the treatment of obesity and to control hunger associated with pro-opiomelanocortin deficiency.
  • Isturisa (Osilodrostat, Novartis Pharms Corp, 03/06/2020)
    Used for adults with Cushing’s disease by blocking the enzyme known as 11-β-hydroxylase (CYP11B1) and preventing cortisol production.
  • Orgovyx (Relugolix, Myovant Sciences GmbH, 12/18/2020)
    It is a GnRH receptor antagonist and is used for the treatment of certain patients with pancreatic cancer.
  • Qinlock (Ripretinib, Deciphera Pharmaceuticals LLC, 05/15/2020)
    Potent pan-KIT and PDGFRα kinase inhibitor. It is the first new drug specifically approved as a fourth-line treatment for advanced gastrointestinal stromal tumor (GIST).
  • Veklury (Remdesivir, Gilead Sciences Inc, 10/22/2020)
    Inhibits SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). It was the first medication in the U.S. for the treatment of patients with COVID-19 infection (hospitalized adults and adolescent).
  • Zokinvy (Lonafarnib, Eiger BioPharmaceuticals Inc, 11/20/2020)
    A farnesyltransferase inhibitor which is used to treat certain patients with Hutchinson-Gilford Progeria Syndrome and Progeroid Laminopathies (Rare conditions caused by certain genetic mutations that leads to premature aging).

Interview with Dr. Nandu Gattu, Senior Vice President, Pharma Analytics, Excelra

It is amply clear that digitization and utilization of data science is playing an important role in all aspects of human lives. Drug discovery and development is no exception. How far we have successfully implemented digitization and data science in drug discovery and development? Is this reality or a far-fetched dream?

Question: With the rapid foray of data science and digital transformation technologies in a pan-industry manner, there seem to be various interpretations regarding the identity and role of these terms in the life sciences and biopharma industry. Can you share your perspective on this?

Nandu: Digital transformation is the buzz word these days, everyone is talking about it. But if we look under the hood, there are two major components:

‘Digitization’ and ‘Digitalization’

  • In ‘digitization’, unstructured and scattered data is converted into a structured and machine-readable format.
  • On the other hand, in ‘digitalization’, advanced analytics such as machine learning or deep learning methods are applied on top of the data to derive value from it.

Digital transformation in pharmaceutical industry is nothing but adaptation of digitization at organizational level and implementation of analytics to accelerate drug discovery and development, to bring better medicines for the benefit of humankind.

Why is data transformation important?

  1. It helps bring efficiency in operations
  2. It enables engagement of the stakeholders effectively in a data-driven approach
  3. It helps uncover new trends thereby paving the way for generation of new ideas and innovation

Until recently, digital transformation seemed like a long-term vision across industries. However, recent global events and circumstances have forced enterprises to embrace it rather quickly.

Question: You alluded to the conversion of unstructured information into structured formats, data analytics driven by AI/ML tools and technologies, and the general principles of data transformation. How do all these diverse elements come together to function in tandem, where does it start and end?

Nandu: Let us understand the journey from data to insights, as this sets the stage for fundamental understanding on this topic.

  • Multiple data points culminate into ‘information’
  • Linking the information together builds ‘knowledge’
  • Identifying patterns in the knowledgebase generates ‘insights’

The end-point of this journey is ‘wisdom’ which dictates what to do and what not to do.


In this journey, everyone tends to focus on the “attractive” analytics piece, majorly driven by AI.

However, it is equally if not more important to focus on the initial part, up to building knowledge, as this forms the foundation of all future analytics.

If enough emphasis is not given to the first steps, we are left with artefactual results where we may not be able to make any sense of it. This initial phase can be broadly termed as ‘data digitization’ that involves structuring, harmonizing and integrating data.

The second phase is ‘data analytics’ where an outcome is predicted by leveraging AI/ML tools on structured data.

Question: How are all these “data” principles applied to the vast, multi-domain life sciences industry?

Nandu: There is a deluge of data in the biopharma industry. If you look at the trajectory from drug target identification all the way to initiation of a clinical study, there are a number of nodes in between.

Each node requires data from various streams such chemistry, biology, discovery technology, DMPK, efficacy, safety, IND enabling studies and much more. In this journey, you can appreciate that a huge volume and a wide variety of data is generated along the way. The data is heterogenous, disparate and complex.

While it’s great to generate such rich datasets, unless we practice digitization principles, the data will become useless in no time.

There are several key aspects of digitization we must consider such as standardization, ontologies, annotation, FAIR data principle practices and data warehouse creation.

Question: Can you shed more light on these various aspects of digitization, and how they are specific in context and practice within the biopharma space?

Nandu: ‘Annotation’ and ‘contextualization’ are complex and multi-layered problems, unique to life sciences.

Let us consider a simple example of protein binding interactions. In one scenario, a chemical may bind to a protein that functions as a target receptor, while in another case, a protein may function as an ion channel that allows specific chemicals to pass through.

Hence it is clear that the relationship between a protein and chemical is context driven and not necessarily the same all the time. A human being can infer such relationships but to structure and digitize this information in a seamless automated manner is a different challenge altogether. These kinds of problems are common in life sciences, whereas in non-life science areas, data relationships are often uniform across situations.

‘Data standardization’ across experiments is another crucial element for performing any advanced analytics. The situation is further confounded today with the availability of many open access heterogeneous databases that pharma companies wish to combine with their proprietary data assets, a task that cannot be performed unless this data is standardized. 

Question: You also mentioned that ontologies and data standards are key aspects to consider within the purview of data digitization in this industry. Can you touch-upon these topics and their importance?

Nandu: Yes absolutely, another important challenge in digitization is the usage of ‘ontologies’. All the important entities such as drugs, diseases, and targets have a large number of ontologies.

For example, if we focus on disease as an entity, we have many ontologies such as ICD, DO, MESH, UMLS etc. However, during data integration we have a hard time mapping data with any particular ontology as there isn’t necessarily a 1-1 mapping in place.

Hence, we must find a way to address ontology-oriented issues.

Regarding ‘data standards’, as many are aware, digitization is a reasonably well accepted practice in regulatory submission in drug discovery and development.

We do use SDTM (Study Data Tabulation Model) in clinical practice and in the recent past FDA has mandated to use SEND (Standard for the Exchange of Non-clinical Data) format for pre-clinical data. We are heading in right direction and while there is a standardized format in regulatory submission, we still have room to improve. The formats for US-FDA and Japan-PMDA submission for example are not the same and this varies from authority to authority.

If we have consistent and well digitized data at the foundation level, we do not have to reinvent the wheel to submit the data to regulatory authorities as per their formats.

Question: Considering the sheer volume and diversity of data generated in biopharma research, how does one approach data digitization? Is there a gold-standard method?

Nandu: This is a pertinent question that can be addressed with a brief overview of the current practices and standards in data digitization. Each has its own merits and demerits.

  • First, there is manual data curation by SMEs- this ensures good quality but yields low volume.
  • Second, is high throughput automated data curation. In this case, machine learning, text mining and NLP can be used for data extraction, integration and standardization. This approach is certainly more volume efficient than manual curation, but data quality may be compromised.
  • Finally, we have semi-automation as a middle ground. This is a more favourable and acceptable method to extract and structure data. Most of the time, we start with automation followed by manual curation, enabling us to infuse context and train systems more efficiently. This further allows us to verify or validate the data to transform it into a machine-readable format.

Question: Having discussed digitization as the preliminary part of the data journey; can you walk us through the final part of the story, data analytics? Where does artificial intelligence come into the conversation?

Nandu: I am sure we all must have experienced in one way or the other that we tend to use the words AI/ML/analytics rather loosely and often interchangeably. However, there are fundamental differences.

  • AI is any program that can sense, reason, act and adapt- could be IoT, robotics or data analytics.
  • ML is a subset of AI wherein the algorithm is trained on data and its performance improves as it is exposed to more data.
  • DL is a further subset of ML where neural networks adapt and learn by themselves.

Irrespective of what we call it, these technologies are useful across the pharma value chain, right from discovery to post-market. For practical purposes however, I will henceforth refer to the term AI in our discussion.

Question: You mentioned AI technologies support the entire pharma value chain; can you share some specific examples of its utility and the major players using AI to optimize drug discovery and development?

Nandu: Sure, there are several AI applications either under development or being implemented in all aspects of the drug discovery and development paradigm including pre-clinical, clinical, manufacturing, supply chain, commercial and post-market surveillance.

As we know, AI is more prevalent or practiced in clinical stage and thereafter, where the data is more structured and standardized. This is another testimony to emphasize the importance of structured data and need of digitization at the beginning of the journey.

I am happy to share that at Excelra, we have been successful in implementing AI methods, having provided various services to our partners towards accelerating their drug discovery and development programs.

Few more examples come to mind where several traditional large pharma companies and even younger biotech start-ups have embraced AI and digital transformation in their R&D efforts:

  • Novartis has digital innovation hubs across several geographies, while on the other hand BI has digital labs housed within a separate entity called BI-X that supports initiatives within the organization.
  • Companies like Lilly and Teva leverage AI for manufacturing, while others like Pfizer are focused on employing AI for optimizing patient engagement.
  • Insilico Medicine an AI-based biotech, worked in tandem with WuXi and Uni. of Toronto, and identified a potential drug in a record time of 46 days which is 10-15 times faster than conventional methods.
  • Another highlight is the collaboration between a pharma company Sumitomo Dainippon and an AI-company Exscientia, who entered the first AI-predicted drug into clinical trials recently against obsessive compulsive disorder. This was done in under a year, whereas a traditional process would have taken up to 4 years to complete.

The last two examples specially provide substantial testimony to the utility of AI in accelerating drug discovery.

Question: Although it is early days; based on your examples, it surely looks like the life sciences industry has been able to successfully implement AI across the broad spectrum of activities in drug discovery and development. What is the reality of the global acceptance and practice of AI in the pharma world? Finally, what are the pitfalls one must look out for and where do you see this transformation going?

Nandu: At the outset, it is indeed noteworthy to acknowledge that pharma and biotech companies are exploring and taking advantage of AI as a mainstream tool to accelerate, optimize and improve numerous processes, functions and stages of drug design and development these days. However, the pharma industry is still lagging in the area of monetization on AI.

I recall a study by McKinsey published in The Economist that compared different sectors and their gains from AI. Pharma was at the very bottom with respect to the % share of total analytics, as well as gains in absolute numbers.

This points towards a huge room for improvement.

A lay person might wonder why pharma is last in the list. The bottom line comes down to what we discussed earlier; that we are presently challenged by data, specifically all the confounding factors we have noted at the beginning of the data journey: heterogeneity, complexity, context driven challenges, lack of standard ontologies etc.

We have to further admit that our field has been relatively slow in transitioning from legacy systems to sophisticated technologies.


Finally, in pharma, purely data science doesn’t feature as a standalone solution; rather, a deep understanding of the life science domain is fundamentally needed to draw meaningful insights.


Having said all this, there is a way forward to really derive synergy from the unison of data, data science and digital transformation in this industry. It is important that we develop standards for digitization, democratize data, adopt new technologies, build cross-functional teams and collaborate with external partners wherever necessary.

Only after we have tackled all these aspects can we leverage the true power of AI and ensure that treatments reach the market faster and cheaper, to impact lives and improve outcomes.


Drug resistance and inadequate response to commonly administered drugs across different therapeutic indications poses a major challenge to clinicians and researchers. This leads to unnecessary heath care costs and is incurring additional research expenditure. Despite marked improvement in new therapies, many patients experience progression of disease or disease recurrence asserting the need for early detection of drug response/resistance by evaluating biomarkers. Certain biomarkers are predictive of drug response and possess high potential for use in general clinical applications for personalized medicine.

Although choosing the right biomarkers associated with drug response and drug resistance presents a major challenge to researchers, it is essential for helping design effective patient stratification strategies. Excelra’s GOBIOM (Global Online Biomarker Platform) helps in understanding clinical implications of genetic alterations and their relationship with gene-drug-disease system by interpreting scientific literature. The GOBIOM biomarker database collates genomic variation data from disparate sources and stores them in a highly structured and easily accessible forms to help researchers gain new insights into tumor biology and predict patients’ responses to treatment.

Variant analysis platform in GOBIOM to query gene variants

The variant analyzer platform in GOBIOM enables the user to study variants within the context of the gene and visualize the gene variant data in UCSC genome browser. The Gene coordinate from the database in GRCh37 or GRCh38 format for example, would be the input parameter in UCSC genome browser, which has various annotation tracks beneath genome coordinate positions, allowing for rapid visual correlation of different types of genomic data.

The ‘Chromosomal View Tool’ in GOBIOM enables search of gene variants by Entrez ID, Chromosome number and Disease name

Gene variants in GOBIOM are represented as a three layered chromosomal view where, the outer layer represents chromosome number, the middle layer represents GRCh38 genomic build and the inner layer represents GRCh37 genomic build. Curated data pertaining to gene variants can be accessed by clicking on the gene coordinate data points in the chromosomal view visualization.

Figure 2: Chromosomal view platform in GOBIOM enables search of gene variants by Entrez ID, Chromosome number and Disease name.

Figure 2: Chromosomal view platform in GOBIOM enables search of gene variants by Entrez ID, Chromosome number and Disease name.

Figure 3: Gene variants search by Disease name. The image on the right side represents gene variants in Breast cancer.

Figure 2: Chromosomal view platform in GOBIOM enables search of gene variants by Entrez ID, Chromosome number and Disease name.

In this manner, focused biomarker databases like GOBIOM can be a very useful resource to identify biomarkers predictive of drug response or resistance, further facilitating selection of right patient population who are most likely to respond to treatment.


“What you measure will improve”- Aron Anderson

A Historical Perspective:

Big data analytics was used as early as in 1600s for predicting epidemics of bubonic plague in Europe. Though records of decision-making from health care data are available from Babylonian times, demographer John Graunt pioneered the principles of statistical analytics in health care which were used for predicting mortality at a population level in London. When mathematician Clive Humby declared “data is the new oil”, the analytics industry was just scratching the surface of what would be a “big data analytics” revolution.

While clinical trials are the cornerstone of research, the huge investments and long turn-around time leaves room for innovation in conducting clinical studies efficiently. Only 12% of drug candidates entering clinical trials are approved for use in therapy. With an average time of ten years for a new drug discovery and an investment of approximately USD 2.6 billion per new molecule brought to the market, the industry is exploring new avenues to cut down time and costs. A blockbuster drug takes approximately 12 years and $4 billion-$11 billion of investment.

Quantum of Data:

“Data that sit unused are no different from data that were never collected in the first place.” – Doug Fisher

Colossal amounts of data are continuously added to the health care space. As of 2019, lists 325,860 studies with locations in 209 countries. In addition, the average hospital generates approximately 665 terabytes of data annually, 20% of which is unstructured forms of images, video, and doctor’s notes. Also, add to this the vast amounts of data generated from Internet of Things in health care.

Big Data Utilization:

“It’s time to move from reactive sick-care to proactive healthcare by default” – Koen Kas

The health care industry has been experimenting with the idea of combining the data generated from different sources to achieve efficiency in drug development across the drug lifecycle. With the vast amounts of data being generated from clinical trials and from real world data sets, comes the promise of shorter drug discovery times, targeted personalized medicine and low cost of drug development. The industry has evolved to accept big data from electronic medical records, patient registries, pharmacies, and claims data sets for prevention, prediction, diagnosis, planning, and, management of health conditions. It is not only used for tracking and estimating safety, efficacy and effectiveness of interventions, but also for improving efficiencies within health systems. Implementation of big data analytics for better diagnosis and disease predictions has the potential to save more than 25% in annual costs by decreasing the hospital readmission rate.

Technical Challenges:

“When we have all data online it will be great for humanity. It is a prerequisite to solving many problems that humankind faces.” – Robert Cailliau

The myriad challenges facing the storage, procurement, privacy, processing, homogenization, analysis, interpretation, and communication of this data are a constant work in progress. Methods for big data management and analysis are being continuously developed especially for real-time data streaming, capture, aggregation, analytics (using machine learning and predictive), and visualization solutions to integrate a better utilization of real-world data (RWD) in health care decision-making. On-site server networks are challenging to maintain and scale. With decreasing costs and increasing reliability, cloud-based storage using IT infrastructure is fast becoming a preferred option by multiple health care organizations. This also enables creation of massive analysable data lakes. Interoperability of shared data is a concern too. Solutions like Fast Healthcare Interoperability Resource (FHIR), public APIs, CommonWell, and Carequality are making data sharing easy and secure.

Compliance Guidelines:

“Having access to information when it’s needed is critical” – Hal Wolf

Technical challenges aside, data security of public health information is governed by rules, termed as HIPAA Security Rules, to guide organizations with storing, transmission, authentication protocols, and controls over access, integrity, and auditing. The “Breakthrough therapy” designation is a United States Food and Drug Administration (US FDA) designation that expedites drug development approved in 2012 to ensure the most efficient possible path to approval is available for manufacturers of drugs. The FDA released draft guidance for communication of healthcare information regarding drugs for reimbursements in the FDAMA Section 114 and Preapproval Information Exchange (PIE) in 2016 for enabling early conversations with payors. In 2018, the FDA issued guidelines regarding analytics and use of real-world evidence (RWE) for drug approval. The Priority Medicines scheme (PRIME) launched in 2016 builds on the existing regulatory framework and tools already available within the European Medicines Agency (EMA) framework for scientific advice and accelerated assessment of drugs. The EMA and European Network for Health Technology Assessment (EUnetHTA) are piloting projects (from 2017-2020) to explore areas of collaboration for identifying synergies for evaluation of interventions and types of accepted evidence for approval and reimbursement.

Proof of Success:

“The goal is to turn data into information and information into insight.” – Carly Fiorina

An impact analysis of breakthrough therapy designation initiative of 2012 found that the FDA approved more than 150 breakthrough therapies and granted more than 332 breakthrough therapy designations since its launch; the initiative also accelerated premarket drug development by 2.2 years. The increasing number of expedited FDA approvals for cancer drugs based on surrogate end points is encouraging many organizations to search for new ways to uncover efficacy and safety data to justify the costs associated with costly treatments. With the growth of data innovation and collaborations, organizations like Friends of Cancer Research (FOCR) are instrumental in driving change and have initiated processes to expedite drug development. FOCR partnered with several data partners, including the American Society of Clinical Oncology (ASCO)’s CancerLinQ, Cota, and Flatiron Health, to conduct pilot studies for creating better defined patient cohorts for improving outcomes and driving efficiencies.

What lies ahead:

“We need to redefine the rules for data ownership and draft something like a digital constitution for citizens across all industries” – Jaana Sinipuro

While we create frameworks and guidelines to drive the use of data and artificial intelligence solutions, data transparency, transparent algorithms, robust data protection rules, and international co-operation across countries and data providers will play an important role in defining the success of big data analytics in health care. Non-profit organizations like Bioethics International track biopharma companies for accountability for trial registration, reporting negative and positive results, publication, and data-sharing. Data transparency and democratization is important to expedite research and customize delivery of efficient seamless health care to all.

Data assimilated from clinical trials and RWE is the future of health care.

Author: Dr. Kavita Lamror (Director – Value Evidence, Excelra)


“The industry is taking a more data-driven and strategic approach to Drug Repurposing.”
Dr. Nandu Gattu – Senior Vice President; Pharma Analytics, Excelra

“Pharma is taking a more of a data-driven and strategic approach to Drug Repurposing,” says Nandu Gattu, Ph.D.,  Senior Vice President, Pharma Analytics and Drug Repurposing at Excelra, a data and analytics company. “Instead of waiting for serendipity driven drug repurposing, companies are evaluating the alternative indications of the drug considering the drug target, what pathways the particular drug is up-regulating and down-regulating; understanding what similar drugs are available; and examining current clinical trials that are being conducted.”

A strategy should include a review of the entire pharmacology, biology, toxicology, clinical trial information, genetics, genomics, and chemistry of that drug in a 360-degree manner, he says. Bringing all this evidence together creates a faster, smoother pathway to finding the science to a new indication. “We put together the information layer by layer, so at the end of the day, we have a very good scientific justification to make a recommendation,” Dr. Gattu says

Excelra recently signed on with Japanese pharmaceutical company Maruho to help the drug maker discover and develop novel therapeutic hypotheses in dermatology, and with Keio University School of Medicine in Japan to explore new therapeutics for a rare indication.

Excelra has a database of about 9 million structure-activity relationship compounds collected over the last 18 years from scientific publications and journals. “It is very useful for us to understand the similar compounds and extrapolate the information,” he says. “But there are two things necessary for all this analysis to be successful: algorithm and data.”

Dr. Gattu attributes the accessibility and the availability of more clinical trial data as a plus for driving a repurposing strategy. Companies no longer have to rely on their own proprietary data. There are myriad public data banks that researchers now have access to, and the computational tools in today’s market provide a way to parse through big data more easily. “There is a ton of a genomic and genetic-oriented data available in the public arena that we use on a regular basis,” Dr. Gattu says. “At the same time, there are quite a number of people opening up their clinical trial data, so this information is also being used.”

Data-driven approaches also include literature mining, which is now possible through text mining and NLP capabilities. This enables mining unstructured articles and receiving output in a structured manner. “This makes it easy to understand what’s going on, so we can connect A to B and B to C,” Dr, Gattu says. “There is always an opportunity for us to link between a drug and new diseases using this data-driven approach.” According to Dr. Gattu, repurposing will always be an opportunity for pharma companies for multiple reasons: drying pipelines, lack of new molecular entities being discovered, new technologies, and more data that provide the setting for repurposing. “New technologies are evolving, but at the same time, pipelines are not improving for individual pharma companies,” he says. “So, in my opinion, drug repurposing is going to be used even after a drug is on the market for 10 years because the more real world evidence that is available will help clarify the picture.”


Model informed drug discovery and development (MID3) is a paradigm of data analytics in drug development which facilitates the development and application of various quantitative approaches like exposure-response, biological, and statistical models to characterize pre-clinical and clinical data. MID3 is usually aimed at improving the quality, efficiency and cost effectiveness of decision making in drug development. The knowledge and inferences from this quantitative framework help in making informed decisions for dosage optimization and provide supportive evidence for efficacy, clinical trial design, and informing policies. MID3 approach enables multidimensional integration of data across targets/mechanisms of actions, molecules/drugs, doses/regimens, indications, trial designs, endpoints, and patient characteristics/populations.1

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Model based meta-analysis (MBMA)

Model based meta-analysis is an important quantitative toolkit in MID3. MBMA is different from conventional meta-analysis as MBMA helps to integrate concepts of pharmacology and biology with statistical concepts.2,3

MBMA serves a range of purposes such as:

  • To increase the number of observations and their statistical power, if Individual trials are too small to give reliable answers
  • To improve estimates of the effect size of an intervention or association
  • To put any one trial into perspective by examining all similar trials by analyzing individual patient level data along with competitor summary level data
  • To assess the generalizability of the conclusions to a more varied range of patients or treatments protocols by examining variability between trials and studies and performing subgroup analysis
  • To resolve uncertainty when reports disagree
  • To identify the need and planning for larger trials of studies, by identifying the gaps in evidence
  • To answer questions not posed at the start of individual trials by finding answers to strengthen submissions on drug efficacy to licensing bodies such as the FDA
  • To retrospectively identify the point in time when a treatment effect first
  • reached conventional levels of statistical significance
  • To assess determinants of publication bias
  • To investigate side effects, often rather infrequent, of a treatment

Use of MID3 approaches are increasing as the wide applications of them are established and are also gaining acceptance and interest from regulatory authorities.4 MBMA has proven applications in the complete life cycle of drug discovery and development from early development activities like candidate and target selection, early clinical phase decisions on candidate development and trial design to decisions during late clinical phase and post approval .1,3

Applications of MBMA

Identifying and preparing essential metadata available from the public domain of biomedical literature, specific to the analysis objective for MBMA requires voluminous efforts.

Analysis-ready datasets for MBMA

Excelra’s expert clinical pharmacology group is helping the Quantitative Pharmacology and Pharmacometrics groups of pharmaceutical companies in building MBMA analysis specific datasets. This is performed with robust and customized, systematic literature review (SLR), following the industry-accepted PICOS methodology.

Case Study

The Case of Competitive Landscape & Go/No-go Clinical Trial Decision for a Big Pharma”


To identify the probability of the success of a new therapeutic antibody for Rheumatoid Arthritis.

About the Client

  • Company- Big Pharma
  • Location- Europe
  • Therapeutic Area- Rheumatoid Arthritis

Client Specification

The client wanted to assess the relative efficacy of its antibody, against competitor marketed biologics for Rheumatoid Arthritis. A customized MBMA friendly dataset was to be developed by curating existing evidence on the efficacy of marketed biologics for Rheumatoid Arthritis.

The Excelra Approach (Methodology)

  • Defined project scope with PICOS methodology for conducting Systematic Literature Research in PubMed.
  • Screened, Labelled and Developed a database for enabling further qualification and selection of
    relevant publications according to PICOS specifications.
  • Additional references were identified following a thorough search across FDA drug labeling
    information and traditional meta-analysis publications. (119 sources identified)
  • A rigorous 3-level Quality Control (QC) process was employed for database development.
  • A customized clinical outcomes database was developed, capturing:
    • Clinical outcomes summary data (Time vs Response)
    • Patient population details (Baseline characteristics, prior and background therapy)
    • Interventions (Dose regimen)
    • Comparator (Dosage regimen)
    • Study design (Sample size)

Excelra's Contribution

  • Excelra refined the in-house literature database (Clinical Trial outcomes Database) for Rheumatoid Arthritis, which included 37 Phase II & III studies describing 13474 patients, 75 Arms, 502 summary points.
  • The Database was updated with each time point data digitized from the illustrative time course curve in each study by expert team.
  • It enabled the client to compare the new compound with the available compounds for Rheumatoid Arthritis
  • The magnitude of response and it’s time course analysis from the Excelra’s databases showed that the antibody had lower chances of success compared to competitive drugs Etanercept and Adalimumab owing to its inferior efficacy profile in RA.

The Excelra Edge


Model informed drug discovery and development (MID3) is an integral part of drug development, especially model based meta analysis (MBMA), where information from published literature is leveraged to learn from the past experience and optimize future clinical trial design.

For more information

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  • Marshall SF, Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation. CPT Pharmacometrics Syst. Pharmacol. (2016) 5, 93–122
  • Fagard RH, Advantages and disadvantages of the meta-analysis approach. J Hypertens Suppl. 1996 Sep;14(2):S9-12; discussion S13
  • Upreti VV, Model-Based Meta-Analysis: Optimizing Research, Development, and Utilization of Therapeutics Using the Totality of Evidence. Clin Pharmacol Ther. 2019 Nov;106(5):981-992
  • Demin I, Longitudinal model-based meta-analysis in rheumatoid arthritis: an application toward model-based drug development. Clin Pharmacol Ther. 2012 Sep;92(3):352-9


Protein implicated in the progression of cardiac disease is a novel biomarker associated with inflammatory status and disease severity in patients with COVID-19

Vertebrate homolog of lin-4 is reported as a novel diagnostic biomarker for cervical cancer, whose expression is significantly lower in cervical cancer patients than those in the healthy controls

Member of the WAP family and encoded by WFDC2 gene is reported as a novel inflammatory biomarker correlated with overall disease severity and the degree of pulmonary dysfunction in patients with cystic fibrosis

Novel biomarker for the diagnosis and monitoring of myasthenia gravis

Bifunctional enzyme with methylene dehydrogenase and cyclohydrolase activity is reported as a novel biomarker for predicting prognosis and pathological stages in patients with esophageal carcinoma

Diagnostics (510K)

Roche Molecular Systems, Inc.

Siemens Healthcare Diagnostics Products Ltd.

DiaSorin Inc.

Euroimmun US, Inc.

Fujirebio Diagnostics, Inc.

Clinical Practice Guidelines

Guidelines on the diagnosis and treatment by European Society for Medical Oncology (ESMO)

Guidelines by the European Head and Neck Society (EHNS), European Society for Medical Oncology (ESMO), and European Society for Radiotherapy and Oncology (ESTRO)

Guidelines on the diagnosis and treatment by the European Association of Neuro-Oncology (EANO)

Guidelines on the diagnosis and management by National Comprehensive Cancer Network (NCCN)

Guidelines for the management by American College of Cardiology (ACC) and American Heart Association (AHA)


Tepotinib for Non-Small Cell Lung Cancer; EMD Serono; FDA

Voclosporin for Lupus Nephritis; Aurinia Pharmaceuticals Inc.; FDA

Cabotegravir for HIV Infection; ViiV Healthcare; FDA

Myovant Sciences; FDA

BioCryst Pharmaceuticals, Inc.; FDA



Apelin-13, a 13 amino acid oligopeptide which is the ligand for the
apelin receptor is reported as a diagnostic biomarker whose levels are
higher in both dry-type age-related macular degeneration (AMD) patients
and neovascular AMD patients compared to the control group.

Transfer RNA-derived fragment tRF-28-QSZ34KRQ590K in plasma exosomes is reported as a potential diagnostic biomarker in pediatric patients with atopic dermatitis.

Spliceosome-associated protein 130, a novel danger-associated molecular pattern is reported as a novel noninvasive biomarker that correlates with disease severity in patients with idiopathic pulmonary fibrosis.

Neuron-specific enolase (NSE), a cell specific isoenzyme of the glycolytic enzyme enolase is reported as a biomarker that correlates with extent of disease and distinguishes responders from non-responders during immunotherapy in patients with Merkel cell carcinoma.

Krebs von den Lungen-6 (KL-6), a type of transmembrane mucoprotein is reported as a novel prognostic biomarker in COVID-19 patients with severe pulmonary involvement.

Diagnostics (510K)

Horiba ABX SAS

Ventana Medical Systems, Inc.

Roche Diagnostics

Roche Diagnostics

Guardant Health, Inc.

Clinical Practice Guidelines

Guidelines on the emergency management of Inflammatory bowel disease from the World Society of Emergency Surgery/The American Association for the Surgery of Trauma (WSES/AAST)

Guidelines on the management of actinic keratosis from the American Academy of Dermatology (AAD)

Guidelines on the management of thymic epithelial tumors from the Oncological Group for the Study of Lung Cancer/Spanish Society of Radiation Oncology (OECP/SEOR)

Guidelines for the diagnosis of asthma in children aged 5–16 years from the European Respiratory Society (ERS)

Guidelines for management of patients with anaplastic thyroid cancer from the American Thyroid Association (ATA)


Aducanumab for Alzheimer’s disease; Biogen; FDA

Sotorasib for Non-small cell lung cancer; Amgen Inc.; FDA

Infigratinib for Cholangiocarcinoma; BridgeBio Pharma, Inc.; FDA

Pegcetacoplan for Paroxysmal nocturnal hemoglobinuria; Apellis Pharmaceuticals, Inc.; FDA

Life Sciences Tech Trends 2021

COVID-19 has had an unprecedented adverse impact on global health and economy at large. While the global economy is navigating the financial and operational challenges, the life sciences industry is at the epicenter of attention as the world awaits an effective vaccination to defeat the pandemic. The response so far has been remarkable as governments, organizations, regulators, researchers, and academia have all come together like never before; to create and share knowledge, supply resources, and provide access to technical skills and technologies. It is noteworthy that drug giant Pfizer and BioNtech got their joint SARS-CoV-2 vaccine approved in less than 8 months, which is a clear example for how such collaborations are bearing fruit. In this article we shall reflect upon the opportunities, strategies and technologies that will continue to impact the life science industry in 2021 and beyond.

1. AI powered drug R&D combined with MLOps

AI powered drug discovery is enabling big pharma & biotechs to change the traditional approach of R&D often taking between 11 – 15 years and with costs now exceeding $3 billion. From drug target identification, lead compound screening, preclinical and clinical trials, to greatly improving the success rate of drug development; AI can really streamline R&D efforts by integrating and processing vast datasets to derive actionable insights.

As running these multiple ML models at scale becomes increasingly difficult, MLOps offers an automated way of developing, deploying and refining over time. It refers to the application of DevOps tools applied over ML models from production to deployment.

Here are some use-cases where AI/ML tools are used in drug discovery and development:

Figure 1: AI/ML use-cases in drug discovery & development

2. Hyper-automation across the value chain from molecule to market

Gartner predicted that by 2024, Organizations will cut their operational costs by 30% by adopting hyper automation techniques along with redesigning their business processes. The life science industry has been relatively slow to catch up to this wave but is quickly picking up as some of the major players have already integrated hyper automation into their business strategy.


Potential areas span across discovery and research, development, manufacturing, sales and marketing, supply chain and distribution. Given the highly regulated nature of the industry, sector automation could potentially revolutionize compliance management, patient service and other supply chain improvements. For example; automation in drug R&D can aid identification of biomarkers and DNA/RNA genomic sequencing. On the manufacturing side it can help with continuous plant monitoring and help fast track decision system, optimize inventory and lead to better management of market demands.

Figure 2: Key enablers for hyper-automation

3. Use of advanced analytics

Pharma companies are transforming their traditional approach of developing medicines to deliver highly personalized drugs to offer the right treatment at the right time. This is being done by analyzing millions of deep, broad and disconnected data sets around a patient coming from EHR, digital data, imaging and multi-omics technologies.


Combined the power of AI and real world data, hypotheses can be generated at scale to improve lab testing efficiencies, helping organizations understand disease, drug effectiveness, speed up search for new indications for existing drugs and optimize pricing decisions based on value that is being delivered.

Figure 3: Types of data analytics methods

4. Creating value from next-generation real world evidence

Recent advances in Real World Evidence (RWE) analytics have made pharma organizations looks beyond just descriptive analysis that helps with basic patient profiling and cohort comparisons. Advanced predictive models in combination with ML, probabilistic models, unsupervised algorithms help understand patient characteristics, disease progression, patient response and any potential risks. This facilitates doctors to intervene at the right time and deliver the right care.

Figure 4: RWE use-cases across the pharma value chain

5. Quantum computing as pharma’s next big disruptor

The life sciences sector has the potential to benefit significantly from quantum computing. Majority of challenges in the life science industry are computationally complex, be it finding relationships among sequences, structures and functions; or determining the interaction of different molecules from drug to the body. Compared to 1% today, by 2023, 20% of organizations will be budgeting for quantum computing projects. This holds a very good potential for transforming data heavy processes, speeding up the drug discovery R&D or simulating clinical trials. Some of the use cases are:

Figure 5: Quantum computing use-cases in drug discovery

6. Adopting FAIRIFICATION: Breaking down data silos, developing machine-ready data

Adopting FAIR data principles goes a long way and Covid-19 has made the industry realize that a big potential exists in collaborations to produce effective drugs for better patient outcomes. FAIRIFICATION helps organizations combine external datasets with proprietary information to gain novel insights using graph technologies.


Adopting better methods for data capture and structuring, complex models for data storage and utilizing advanced platforms to auto-identify data relationships, are signs that organizations are prepared for tomorrow, where the data is ready for machine consumption.

Figure 6: Consumption patterns of data lakes


As global economies continue to recover and are looking at innovations to bridge technological gaps, Covid-19 has only accelerated the process and has made us more adaptable and responsive. Organizations are looking at realigning their traditional corporate strategies and are adopting a platform-first strategy that is dynamic to handle a wide range of uncertainties.