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Research & Development

Transform drug discovery research and clinical development

Transform drug discovery research and clinical development

Combine data, AI-driven analytics, standards, and consulting for next-generation clinical trials.

Reimagining drug discovery research and clinical development via data and advanced analytics

It takes an average of 10 years for a new drug to come to market, with clinical trials alone taking 6–7 years. Moreover, the overall probability of clinical success is estimated to be only about 12%, and the average cost to research and develop each successful drug is high, estimated at $2.6 billion. The inability to demonstrate safety or efficacy, flawed study design, participant dropouts, unsuccessful recruitment, etc. have contributed to the low success rate of clinical trials.

However, as the world advances to a more digitalized era, clinical trials are transforming by adopting new technologies and data-driven insights to:

  • progress towards virtual or decentralized clinical trials
  • improve the clinical trial cycle time and effectiveness
  • proactively monitor adverse event and early warning signals
Reimagining drug discovery research and clinical development

Read Blog / The three benefits of clinical data management systems in the clinical study process

Our R&D service offerings

Leverage Axtria’s deep domain and consulting experience along with AI-driven technology solution
to support clinical development.

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Data fitness assessment

Data fitness assessment

  • Identify data consistency, quality, and visibility gaps
  • The framework of process and governance to improve clinical data management
  • Ensure regulatory compliance of the data sets through its journey

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Standards management

Standards management

  • Clinical Data Interchange Standards Consortium (CDISC) standards management from protocol design to regulatory submission
  • Enable trial optimization – resulting in faster outcomes
  • Provide automated and consistent mapping with data lineage

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Events monitoring

Events monitoring

  • Support the regulatory submission process and adverse event (AE) reconciliation
  • Enhanced safety monitoring and corrective actions to increase the trial’s chance of success

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Advanced analytics

Advanced analytics

  • Solve the clinical data aggregation and visibility constraints for better outcome
  • Provide robust controls to implement data quality and governance
  • Enable data validations across clinical data sets as per business rules

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Next generation clinical data management system (CDMS)

Next generation clinical data management system (CDMS)

  • Site selection using both analytical and qualitative techniques to determine the most favorable location for a clinical study operation
  • Signal detection, monitoring, evaluation, and reporting of safety signals in AE

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Data verification and validation

Data verification and validation

  • R&D systems and their data sets follow GxP compliance
  • Verification and validation provide data compliance for all the above offerings

Our solutions for the unmet needs in R&D life sciences

Clinical program access

  • Data fitness assessment
  • Events monitoring
  • Advanced analytics
  • Data verification and validation

Clinical program access

How can clinical programs be made available to patient populations across the globe?

Agile clinical development

  • Data fitness assessment
  • Events monitoring
  • Standards management
  • Data verification and validation

Agile clinical development

How life sciences R&D can act upon the information gathered during the clinical trial to advance the clinical trial rapidly ahead or pivot, if needed, in certain situations?

Clinical program access

  • Advanced analytics
  • Next generation CDMS
  • Data verification and validation

Digital trials

How is data collected, sourced, and made compliant and secure when collected right at the source and before it is transferred over to the trial sponsor?

Analyzing R&D strategy

  • Data fitness assessment
  • Advanced analytics
  • Next generation CDMS
  • Data verification and validation

Analyzing R&D strategy

How does life sciences R&D examine, analyze, and predict the future of a compound for potential investment?

Our work in clinical development

Leverage patient-centric, advanced technology solutions that redefine drug development to improve the efficiency
and outcomes of clinical trials.

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CDMS enablement

Axtria partnered with a global pharmaceutical company to transform CDMS from an outsourced partner to a new platform managed by the company’s internal IT services organization. The effort resulted in quicker turnaround time and enhanced data exchange speed (94% faster)​, better user experience, secure data sharing and access​, and real-time application log monitoring to detect issues​.

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Clinical data processing solution for biomarker extraction

A diversified healthcare company that supports community-based specialty practices across the US partnered with Axtria to use electronic medical record (EMR)/electronic health record (EHR) data to get a complete patient view and deliver clinical insights to physicians. Axtria built a highly sophisticated solution leveraging optical character recognition (OCR), natural language processing (NLP), and machine learning to enable easy dissemination of patient insights to physicians​ to aid R&D and improve the delivery of treatments to patients​.

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Study management enrollment/ engagement prediction system

A healthcare provider collaborated with Axtria to develop an advanced analytics-driven study management enrollment/engagement prediction system to identify trends in patient data using large volumes of high-velocity EMR and wearable device data​. Several data sources were combined to provide patient health monitoring insights and alerts to a patient and her physician. The effort resulted in more frequent and actionable insights to stakeholders, including doctors, hospital network, and network administrators​.

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Social listening monitoring for adverse events

For the clinical trials group of a leading pharmaceutical company, Axtria built an adverse event reporting (AER) engine to automate the identification of adverse events using various natural language processing (NLP) and supervised machine learning techniques​. The effort resulted in an 80% reduction in manual identification effort​. The company can also configure the solution to provide insights for marketing and compliance.

Impact to make for efficiency gains in the research and clinical development process

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Progress towards real-time data collection

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Find efficiencies (2x) to bring down lag times

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Move towards hybrid trials

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Prescriptive insights to change the way trials are conducted and gain efficiencies

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