Contributors : Bindu Ajithkumar
The European biotechnology sector is expanding rapidly, contributing to an estimated €106 billion per year to the continent’s economy. However, its progress is hampered by the European Union’s (EU) complex regulatory framework.
The European Medicines Agency (EMA) provides centralized medical approvals, although individual member states retain their own regulations for market access, reimbursement, and pricing. These limits lead to significant financial stress and delays for biotech businesses seeking to launch novel treatments to the market.
The Biotechnology Sector: Challenges in Europe
Some biotechs are flourishing, while others are not. Innovation is thriving but only those companies with a proven management team and strong clinical data have been able to attract the interest of Big Pharma partners and venture capital (VC) investors. There are numerous regulations throughout the 27 EU member states which also significantly hinders the operations of biotech enterprises. Achieving substantial market penetration is further hindered by the varied price, reimbursement, and access rules in each nation.
While the EMA provides centralized drug approval, this does not guarantee automatic access to individual markets, with biotech companies often waiting over a year for full market access in some countries. In key EU markets, delays for reimbursement decisions can extend up to 6 months, and on average, companies face a 400-day wait for reimbursement approval after securing EMA marketing authorization. This legal fragmentation impedes the expeditious release of new pharmaceuticals throughout Europe by increasing operational costs and impeding innovation.
Recent Legislative Progress Recent changes to EU legislation, notably in the context of orphan drug policy, are designed to enhance the efficiency of drug approval processes. However, these enhancements also introduce additional challenges in terms of compliance and pricing incentives. Orphan drugs, while benefiting from certain regulatory incentives, still face delays due to fragmented national reimbursement schemes. Almost 75% of biotech companies report difficulties with navigating the pricing regulations across the European member states, which can increase costs by 20-30% during the commercialization phase. These extra constraints may pose significant financial challenges for organizations looking to market innovative biotech technologies.
Big Data’s Impact on Drug Development
Addressing regulatory fragmentation and data disparity.
The system’s fragmented architecture commonly causes delays in regulatory submissions due to the unstructured nature of data collecting and processing among EU member states. Without standardized data formats, biotech companies struggle to compile the necessary documentation for timely approvals. The disorganization of data across borders adds to delays, with firms frequently encountering inconsistent requirements for clinical trial data, safety reports, and patient access plans.
The use of AI and predictive analytics may substantially streamline the regulatory filing process. By analyzing vast volumes of data to foretell regulatory obstacles, artificial intelligence models have the potential to enhance business growth processes and ensure compliance with regulatory standards. Biotechnology firms may potentially reduce their filing timeframes by 50% with AI. This could also facilitate their entry into the market half as quickly.
Predictive modeling with ADME data
It’s estimated that nearly one out of every two drug candidates will fail at the clinical trial stage due to insufficient efficacy, and up to two out of every five have previously failed due to toxicity. Regulators and researchers now recognize that, in addition to pharmacological properties, absorption, distribution, metabolism, and excretion (ADME) studies are critical to a drug candidate’s success. For over a decade, pharmaceutical companies have used rule-based filters such as Lipinski’s rule of five to avoid undesirable ADME profiles. More recently, they’ve begun to rely on predictive modeling.
Predictive models are used to assess a drug’s ADME profile, so data scientists require high volumes of ADME data to train their algorithms. Data volume is crucial, but so too is data diversity. The more unique compounds in a data set, the greater the probability of accurate predictions.
Inaccurate predictions can have serious consequences, not least in the wasted expense of significant time and money. Yet it is rarely the models and algorithms that are responsible for the inaccuracy. More often than not, the culprit is the data.
One of the key factors determining data quality is the number of unique compounds included in the set. Given the importance of accurate ADME predictions in drug development, it’s imperative that ML models are trained on ADME data with a high number of unique compounds. To increase confidence in a model’s predictions among stakeholders facing go/no-go decisions about progressing potential drug candidates, data scientists must choose a data source with the requisite level of structural diversity across ADME parameters.
A critical domain for Real-World Evidence (RWE) in regulatory submissions is the use of synthetic data to substantiate clinical trial results. Biotech businesses may enhance their submissions with real-world evidence if they can demonstrate the practical relevance of their findings. Regulatory applications using real-world data may enhance approval rates by 20% or more. Biotech businesses demonstrate compliance with the regulation by consolidating patient information, research findings, and entry criteria from many EU states into a unified dataset.
Excelra’s GOSTAR® for EU Compliance and Pharmaceutical Research
The GOSTAR platform provides access to Excelra’s extensive Structure-Activity Relationship (SAR) database, including the bioactivity, toxicological profiles, and chemical structures of over 10 million chemicals.
Biotech businesses may speed the research process by using precise and confirmed information using GOSTAR’s extensive dataset. Biotech companies are equipped with reliable and current information via GOSTAR, which incorporates over 80,000 new bioactivity data points annually.
Biotech companies can leverage GOSTAR’s advanced predictive analytics to identify prospective lead compounds and foresee probable regulatory challenges. Utilizing curated data and predictive algorithms enables firms to assure compliance with regulatory requirements, hence minimizing clearance delays.
New drug development may be expensive for biotech businesses, but with the use of predictive analytics, they might save 15% to 20% of that cost.
To comply with regulations set by the European Union, GOSTAR employs systems that are designed to meet certain standards. This simplifies the intricate legal environment for biotechnology enterprises. The platform’s simplified regulatory filings expedite the authorization of pharmaceuticals by providing compliance-ready data, thereby reducing delays.
Endnote
The biotechnology business in Europe is confronted with significant challenges due to the fragmented regulatory landscape. Recent changes designed to expedite operations have inadvertently increased complexity. Excelra’s GOSTAR is a critical tool for addressing these challenges.
GOSTAR’s enormous datasets, predictive analytics, and focus on regulatory compliance make it an invaluable resource for biotechnology businesses looking to speed pharmaceutical development and establish a strong market presence in Europe.
Collaborate with Excelra at BioTech-X EU to understand how GOSTAR can help your organization navigate the complexities of European biotech law and accelerate market entry.
