Category: TL;DR

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TL;DR

PHUSE Automation SDE

PHUSE Automation mindmap

Enabling end-to-end Automation in Analysis and Reporting

PHUSE US Single Day Event 21-MAY-2021

Summary

The PHUSE US Spring 2021 SDE was an interesting cross-section of perspectives on automation of Analysis and Reporting. My takeaway messages were:

  • End-to-end machine readable standards are still a work-in-progress, and there is not a clear path to standardisation around e-Protocol and e-SAP
  • CDISC aim to make it simpler to implement software that automates standards (rather than publishing standards as ‘600-page pdf documents’); this is based on the proprietary CDISC Library
  • Key implementation challenges include: Managing change, Budgets (it takes longer than you think) and Silo business processes
  • PHUSE have a mature set of safety reporting deliverables, including SAP definitions, statistical methods and visualizations. This can provide a solid basis for automation projects.

DISCLAIMER: I missed one session by Farha Feroze (Symbiance) on Automated TFL Moch Shell Generation using AI Techniques (ML/NL), so is not included in this report!

Future State

The day was (excellently!) chaired by Bhavin Busa, and Gurubaran Veeravel, who kicked-off with reference to the CDISC 360 ‘Future State – Analysis and Reporting’, noting that “we are not there yet!”

Future state analysis and reporting

This is a future-state vision that is based on automating the current reporting pipeline. Currently ADaM and TFL programs are written manually.

CDISC Standards

Anthony Chow presented an overview of the CDISC Library, and current/future projects related to Analysis & Reporting

CDISC aim to make it simpler to build software that automates standards-based processes.

  • The CDISC Library provides an application programming interface (API) to ‘normative metadata’ on all CDISC standards
  • CDISC is working with open source projects on tools to access CDISC Library
  • CDISC have Analysis & Results projects ongoing and about to start recruiting members: MACE+ project, Safety User Guide, and Analysis Results (ARM) Standard development.

Path to Automation

Andre Couturier & Dante Di Tommaso (Sanofi) gave an insightful presentation on the challenges facing when implementing end-to-end automation (for a start.. don’t call it automation!)

  • It is key to communicate a clear vision and ‘sell upwards’
  • Key challenges include change management, Silo business processes, and budget (it takes longer than you think!)
  • Decide whether existing processes are ‘Deterministic’ or ‘Intelligent’ – Deterministic processes are candidates for automation, and Intelligent processes can be ‘facilitated’
  • Sanofi use the acronym MAP (Metadata Assisted Programming) to describe the change in approach

Safety Reporting

Mary Nilsson (Eli Lilly) provided a comprehensive overview of the work that PHUSE has done on Safety Reporting. A comprehensive set of deliverables are available phuse.global

  • The two working groups are: Standard Analyses and Code Sharing (pre-2020) and also Safety Analytics (post-2020)
  • In addition there are training videos covering pooling data, safety analytics and for integrated reporting
  • The deliverables include SAP text, statistical methods, and visualisations
  • This is a body of knowledge that can provide a solid set of ‘source documentation’ for automation projects

Traceability and dependency

Gurubaran Veeravel walked-through how Merck perform impact analysis when standards change – specifically to determine program dependencies and variables used.

R demo – TFL Generation

Jem Chang & Vikram Karasala (AstraZeneca) presented how R programs can create RTF outputs with the same layout as existing (SAS) outputs. Alternative/existing R packages are available, and the pros/cons of each were discussed.

Categories
TL;DR

Intelligent clinical trials

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Transforming through AI-enabled engagement

This Deloitte Insights report, published in Feb 2020, examines the AI technologies in Clinical Trials and is the third in a series, the first is an overview of AI in biopharma and the second is on AI in drug discovery.

If you haven’t read the Intelligent Clinical Trials report then don’t worry! This article aims to provide the key points.

AI has the potential to transform key steps of clinical trial design from study preparation to execution towards improving trial success rates, thus lowering the pharma R&D burden.

Artificial Intelligence for Clinical Trial Design

Main application areas

  1. Protocol design
  2. Patient selection
  3. Recruitment and retention

..Where the use of Real World Evidence (RWE) is used to enrich trial-specific data to optimise patient search, recruitment and retention.

The use of real-world data brings challenges including data interoperability and adoption of open and secure platforms, and consumer-driven care.

FDA guidance to industry

The FDA have published guidance for industry entitled “Enrichment Strategies for Clinical Trials to Support Demonstration of Effectiveness of Human Drugs and Biological Products.” The purpose of this guidance is to assist industry in developing enrichment strategies that can be used in clinical trials.

Clinical trials of the future

FDA is already planning for a future in which more than half of all clinical trial data will come from computer simulations.

This is a future where phase 1 trials are done in-silico i.e. using a simulation of a human body, and phase II/III trials become remote decentralised clinical trials (RDCT) which use AI-enabled technologies to allow bigger, more diverse and remote populations to participate – as envisioned by the European Innovative Medicines Initiative Trials@Home project, launched in December 2019.

Real-world data (RWD)

In April 2020, Apple and Google announced a partnership to enable interoperability between Android and iOS devices using apps from public health authorities.

In the coming months, Apple and Google will work to enable a broader Bluetooth-based contact tracing platform by building this functionality into the underlying platforms.

This is an indication of the scale of real-time, real-world data that will be available to enrich regulated clinical trials in the future.

Summary

  • The benefits of AI for Clinical Trials centre around the optimisation of protocols, patient recruitment and retention
  • This change will involve enriching clinical data with new sources of real-world data (RWD)
  • Phase I trials will be run as in-silico computer simulations, and phase II/III will become virtual, decentralised clinical trials
  • Regulatory authorities have already started publishing guidance to industry.
  • For the next few years, RCT’s are likely to remain the gold standard for validating the efficacy and safety of new compounds in large populations.