Introduction

Bringing a drug to market costs more than $2.6 billion and can take up to 15 years—a reality often accepted as the norm. But new research and industry insights highlight a lesser-known roadblock: inefficient deployment of data infrastructure across drug development workflows.

In a LinkedIn poll conducted by Elucidata, the top challenges cited by researchers were:

• Integrating multi-omics data (42%)
• Ensuring specificity and accuracy (37%)

Yet one critical issue was largely overlooked—deployment.

In the context of drug development, deployment refers to the process of efficiently integrating and scaling data solutions across research, clinical trials, and regulatory workflows. This includes everything from managing cloud-based bioinformatics pipelines to automating regulatory workflows, efficient deployment is the key to transforming data into faster decisions.

The accelerated timeline for the development of the COVID-19 vaccines is a great example of the impact of a fully optimized deployment pipeline. Cloud-powered data processing enabled real-time insights and allowed global collaboration. At the same time, regulatory agencies fast-tracked reviews without compromising safety. This resulted in the development, testing, and authorization of COVID-19 vaccines in less than a year, proving to be an exception to the norm of prolonged drug development timelines.

Outside of a global emergency, however, life-saving treatments for cancer, Alzheimer’s disease, and rare diseases remain stuck in inefficient pipelines. Obstacles such as slow processing, fragmented infrastructure, and regulatory bottlenecks prevent scientific progress achieving the pace it should.

Elucidata recognizes this gap and is leading the conversation on fixing it. In a previous blog on resilient deployment pipelines, we explored how scalable, automated data solutions can eliminate bottlenecks in data processing pipelines. Now, we dive deeper into the specific deployment challenges slowing time-to-market for life-saving drugs, and how addressing them can bring new therapies to patients sooner.

How a Drug Development Pipeline Works

Developing a new drug is a complex process comprising multiple phases. Each phase generates massive volumes of data, requiring efficient collection, integration, and analysis to drive decisions. Any delays in data processing, compliance workflows, or infrastructure scaling extend the time-to-market for life-saving therapies.

The drug development pipeline consists of four major phases:

1. Target Identification & Validation involves identifying molecular targets (e.g., proteins, genes, mRNA transcripts) that play a key role in disease pathways. This process integrates multi-omics data (genomics, transcriptomics, proteomics, metabolomics) with high-throughput screening (HTS) to evaluate thousands to millions of compounds for potential interactions. AI/ML models for protein-protein interactions, protein structure prediction, and disease classification accelerate this process.
   
   
2. Preclinical Research evaluates drug candidates in in vitro (cell-based) and in vivo (animal model) systems to assess safety, efficacy, pharmacokinetics, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) before human trials. This phase combines lab-based experiments (such as cellular imaging, 3D organoid models for physiologically relevant testing, and CRISPR-modified animal models for disease studies) with computational modeling to refine drug properties. Molecular dynamics simulations predict drug-target interactions at the atomic level, physiologically based pharmacokinetic modeling forecasts drug metabolism, and AI-based toxicity prediction models screen compounds for safety risks early. These approaches help eliminate non-viable candidates and optimize promising drug formulations for clinical trials.
   
   
3. Clinical Trials (Phases I–III) evaluate a drug’s safety, dosage, and effectiveness in human subjects before regulatory approval. Phase I tests safety and pharmacokinetics in a small group of volunteers. Phase II assesses efficacy and side effects in patients with the target disease, and Phase III confirms large-scale effectiveness in diverse populations across multiple locations. These trials generate vast datasets, including pharmacokinetic profiles, biomarker analyses, Electronic Health Records (EHRs), medical imaging, and real-world patient responses.
   
   
4. Regulatory Approval & Post-Market Surveillance ensures that a drug is safe, effective, and compliant before and after market entry. Companies submit clinical trial reports (CTR), common technical documents (CTD/eCTD), adverse event reports, and manufacturing quality data to regulatory agencies like the FDA, EMA, and PMDA for approval. After commercialization, long-term safety monitoring relies on real-world evidence (RWE) from EHRs, insurance claims, patient registries, and wearable devices, along with spontaneous adverse event reporting systems (e.g., FAERS, EudraVigilance). 

Each stage is heavily data-driven, but inefficiencies in deployment can hinder progress. The following sections outline the major deployment challenges that impact time-to-market for new drugs.

Key Deployment Challenges in Drug Development

ETL Pipeline Inefficiencies: The Hidden Bottleneck

ETL (Extract, Transform, Load) pipelines are the foundation of biomedical data workflows, enabling researchers to ingest, clean, and analyze datasets efficiently. However, poorly optimized ETL processes create delays at every stage of drug development.

• Slow Extraction: Poorly optimized ETL slows ingestion from FASTQ, BAM, VCF, HL7, and FHIR formats
• Inefficient Transformation: Data normalization, batch correction, and metadata standardization often require manual intervention, delaying AI/ML analysis.
• Delayed Loading: On-premise storage limitations or poor cloud optimization slow down real-time data access.

Slow ETL pipelines delay AI-driven drug discovery, hamper clinical trial data ingestion, and stall regulatory submissions due to non-standardized formatting. These inefficiencies force researchers to manually preprocess data, extending timelines and increasing the risk of errors.

Infrastructure Fragmentation: Disconnected Systems Slow Progress

Most pharma and biotech companies operate in hybrid environments: some data is stored on-premise, while cloud-based platforms handle computational modeling and analytics. However, without seamless integration, workflows break down.

• Data silos: Different teams (wet-lab, clinical, regulatory) use separate databases, making it difficult to share information.
• On-premise vs. cloud inefficiencies: Computationally intensive workloads (e.g., protein structure modeling) may lack dynamic cloud scaling.
• Lack of real-time data access: Scientists often rely on manual file transfers, increasing errors and inefficiencies.

Without seamless integration, cross-functional collaboration suffers, leading to duplicate work, slow data sharing, and delays in decision-making. On-premise limitations prevent rapid scaling of high-performance computing (HPC) workloads, slowing AI/ML models and real-time data analysis.

Lack of Automation in Compliance & Regulatory Workflows

Regulatory agencies require meticulously formatted, validated, and traceable datasets before approving a new drug. However, many organizations still rely on manual compliance processes, increasing errors and delays.

• Manual data validation: Researchers must manually cross-check clinical trial data to ensure adherence to CDISC formats (e.g., SDTM, ADaM).
• Inefficient audit trails: Companies lack automated version tracking, making it harder to prove data integrity.
• Submission rejections: Regulatory agencies reject submissions due to formatting errors, requiring costly rework.

These result in negative consequences such as delay in regulatory approvals by months or years. Often, companies end up resubmitting data multiple times, extending time-to-market.

Poorly Managed Data Security & Access Control

Strict regulations like HIPAA, GDPR, and FDA 21 CFR Part 11 require controlled access to sensitive patient and research data. However, many companies fail to implement scalable, secure access policies.

• Overly restrictive access control: Researchers cannot access the data they need, slowing down workflows.
• Lack of audit logging: Compliance violations occur because data modifications aren’t properly tracked.
• Inconsistent encryption standards: Data is transferred in unsecured formats, creating compliance risks.

Overly restrictive access controls slow research workflows, while weak security policies increase compliance risks (e.g., HIPAA, GDPR violations). Researchers waste time requesting permissions and manually transferring files, reducing productivity and increasing data silos.

Inefficient Resource Scaling for AI/ML Workloads

Modern drug discovery relies on deep learning models, molecular simulations, and AI-driven compound screening which demand massive computing resources. However, resources do not always meet the requirements for optimal scaling.

• Under-provisioning: Drug discovery teams lack GPU/TPU access, forcing delays in model training.
• Over-provisioning: Unoptimized cloud allocation increases costs without improving efficiency.
• Manual workload distribution: AI teams must manually allocate compute resources instead of using auto-scaling orchestration (e.g., Kubernetes, AWS Batch).

Under-provisioning delays, as deep learning models take weeks instead of days to train due to compute shortages. Over-provisioning leads to escalating cloud costs without optimizing workload efficiency.

These issues highlight how drug development pipelines can get clogged, resulting in massive losses in time, money and scientific discovery for the biotech industry. Overcoming deployment challenges should be the top priority for drug development companies.

Solutions to Mitigate Deployment Challenges: The Elucidata Approach

To accelerate drug development, organizations must streamline data pipelines, automate regulatory workflows, enhance data quality, and optimize computational efficiency. Elucidata’s Polly platform provides a cloud-native, scalable infrastructure that enables faster, more secure, and standardized data processing at every stage of drug discovery.

Scalable & Cloud-Native Infrastructure for Faster Data Processing

A cloud-native and scalable architecture ensures that data flows seamlessly across research pipelines, clinical trials, and regulatory submissions.

Key Strategies:

Real-Time and Incremental Data Processing: Traditional batch processing occurs at regular intervals, such as at the end of the day, when data is collected in bulk and processed together, leading to delays. In contrast, streaming ETL frameworks like Apache Kafka, Spark Streaming, and Apache Flink work on tasks the moment they arrive, continuously ingesting and processing data in real time. This ensures that AI models receive up-to-date information instantly, enabling faster and better decision-making in drug screening, clinical trial monitoring, and biomarker discovery.

Cloud-Native & Hybrid Deployment: Cloud-native deployment leverages platforms like AWS, GCP, and Azure to manage all infrastructure, storage, and computing in the cloud, enabling automatic scaling for AI/ML workloads without the need for on-premise hardware. In contrast, hybrid deployment combines on-premise storage with cloud-based high-performance computing (HPC), allowing organizations to keep sensitive data locally while using cloud resources for large-scale analytics. This is especially important in drug discovery, where AI-driven research requires massive computational power while maintaining security and regulatory compliance with standards like HIPAA, GDPR, and FDA 21 CFR Part 11.

Containerized Workflows for Portability & Reproducibility: Docker ensures workflow portability by packaging applications with all dependencies, eliminating compatibility issues across different environments. Kubernetes extends this by orchestrating these containers at scale, automatically managing deployment, scaling, and resource allocation. In drug discovery, this combination enables reproducible AI/ML models, seamless multi-cloud execution, and automated failover recovery, all of which ensure that high-throughput sequencing, bioinformatics, and computational drug screening can run efficiently without manual intervention.

Elucidata in Action

A precision oncology company running high-throughput screening on multiple cell lines faced significant challenges in managing fragmented datasets across different teams and timelines. Researchers had to manually retrieve and process historical Excel files, causing delays in comparative analysis and drug candidate identification.

Polly's Drug Atlas Solution:

• Built an automated ETL pipeline to ingest, standardize, and harmonize cell viability assay data across experiments.
• Integrated a metadata annotation framework, making all historical and real-time data findable in seconds rather than hours.
• Enabled scientists to generate custom reports and comparative analyses instantly via a GUI-based dashboard.

Impact:

• Reduction in time required for single analyses by about 25 times.
• 3 months to ingest all historical data, allowing instant querying of drug-cell line interactions.

Automated Compliance & Regulatory Workflows

Regulatory hurdles often arise due to manual validation, inefficient audit trails, and inconsistent data formatting across submissions. Automating compliance workflows minimizes human errors, ensures standardization, and accelerates approvals.

Key Strategies:

Automated Data Validation & Audit Logging: Tools like Great Expectations, DataHub, and MLflow enable real-time data quality checks, track modifications, and ensure adherence to CDISC (e.g., SDTM, ADaM) and regulatory submission formats (eCTD).

Secure & Compliant Data Sharing: Implementing Role-Based Access Control (RBAC) ensures secure, permissioned data access, preventing unauthorized modifications while complying with HIPAA, GDPR, and FDA 21 CFR Part 11.

Interoperability with Regulatory Submission Systems: Ensuring compatibility with eCTD standards and automating submissions reduces errors, avoids rework, and accelerates drug approval timelines.

Data Quality & Standardization at Scale

Noisy, incomplete, and unstructured data skew AI/ML predictions and delay decision-making. Ensuring standardized, high-quality datasets from the outset improves downstream analysis and prevents computational inefficiencies.

Key Strategies:

Standardized Data Formats & Metadata: Adopting FAIR (Findable, Accessible, Interoperable, Reusable) principles^[1], along with structured file formats (e.g., Parquet, Delta Lake), ensures data consistency and accessibility.

AI-Driven Anomaly Detection: Using TensorFlow Data Validation and Amazon Macie, organizations can automatically detect data inconsistencies, outliers, and missing values, preventing errors from propagating through analysis pipelines.

Elucidata in Action

A Cambridge-based RNAi therapeutics company faced major inefficiencies in identifying and curating high-quality single-cell datasets for gene silencing studies. Public datasets were low quality, difficult to find, and required extensive manual review.

Polly's Single-Cell Harmonization Solution:

• Used programmatic search algorithms to retrieve more than one million cells and 5,000 rare disease samples from diverse repositories.
• Developed a cell-type re-annotation pipeline, improving annotation accuracy across 20 muscle and 25 kidney cell types.
• Implemented AI-driven batch correction and metadata enrichment, ensuring consistency across datasets.

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Impact:

• Two times faster identification of potential RNAi target genes.
• 1.8 million single cells harmonized across 43 datasets and 5 tissues.
• Approximately 1500 hours saved on dataset sourcing, metadata annotation, and cell-type classification.

Optimized AI/ML Workloads for Drug Discovery

AI/ML models require high-performance compute environments, but many organizations over-provision or under-utilize resources, leading to inefficiencies. Optimizing AI infrastructure and data pipelines ensures cost-effective, high-speed model training and inference.

Key Strategies:

Dynamic GPU/TPU Resource Scaling: Auto-scaling frameworks like AWS Batch, Google Vertex AI, and Kubernetes dynamically allocate on-demand GPUs/TPUs, reducing unnecessary cloud spending while maintaining performance.

Efficient Data Caching & Preprocessing Pipelines: Feature stores like Feast and Tecton allow AI models to access preprocessed datasets instantly, preventing redundant computations.

Optimized ML Models for Faster Drug Screening: Techniques such as model quantization, pruning, and distillation reduce computational overhead while maintaining predictive accuracy for virtual screening, molecular docking, and biomarker discovery.

Conclusions

Fixing deployment inefficiencies is about optimizing IT infrastructure. But for drug development companies, it is also about accelerating research, reducing costs, and delivering life-saving treatments faster. Elucidata’s Polly platform provides a scalable, automated, and regulatory-compliant deployment solution, ensuring that data pipelines are optimized for speed, security, and scientific accuracy.

Want to see how Polly can streamline your deployment workflows? Schedule a demo with Elucidata today.