TrialTwin :: Our Vision

Our Point of View

We have a strong point of view of where the pharma industry can go in the next 10 years.

First we outline the lifecycle of a medical device or pharma product:

  • Market Research: what disease / condition to target?

  • Novel Compound: substance with expected positive impact

  • Regulatory Submission: data submitted to seek clearance

  • Approval: regulatory clearance to market in target jurisdiction

  • Withdrawal / recall: end of the device's / drug's usable life

TrialTwin - Our Vision - Lifecycle of Medical Device or Pharma Product

Our long-term goal is to use TrialTwin to manage both Drug Development Plans ("DDP") and Target Product Profiles ("TPP") for pharmaceutical companies.

An Integrated Drug Development Plan ("DDP") for a new drug program:

  • improves efficiency

  • reduces costs

  • shortens timelines

  • increases probabilities of success

And a Target Product Profile ("TPP") helps to coordinate the efforts of experts through the different phases of a new drug program:

  • Non-clinical

  • Clinical

  • Regulatory

  • Manufacturing

  • Commercial

Predicting Outcomes | 10-Year Vision

TrialTwin - Our Vision - Lifecycle of Medical Device or Pharma ProductTrialTwin - Our Vision - Prediction levels

Prediction Level 01

Simulate the full regulatory submission package from the chemical structure of the new compound. * Historical data of chemical structures cleared earlier * Synthetic data of Subjects. Even before FPI.

Prediction Level 02

Simulate taking a new compound all the way out to regulatory approval. * Historical data of approval pathway followed by previous devices / drugs in similar class.

Prediction Level 03

Simulate the entire life-cycle of a new compound out to the marketing and revenue-producing stage. * Historical data of usage, reimbursement * Synthetic simulation of target population

Phase I | Build a Synthetic Population

Synthetic Population:

  • Create scientifically-accurate Synthetic Persons.

  • Create full Synthetic Personal Health Record for each Person:

    • conditions

    • devices

    • drugs

    • lab results

  • Simulate hospital visits from Electronic Health Record ("EHR")

TrialTwin - Our Vision - Phase I | Build a Synthetic Population

Phase II | Run a Synthetic Clinical Trial

Start Data Management early:

  • Build study in EDC

  • With trial-specific parameters

  • Populate each CRF with realistic, fake synthetic data

  • Simulate RWD coming from non-CRF data sources

TrialTwin - Our Vision - Phase II | Run a Synthetic Clinical Trial

Phase III | Synthetic Submission

Start analysis before FPI:

  • Build, test, and validate entire analysis process early

  • Prepare a test full submission with synthetic data

  • Generate all documents, reports, tables and figures

TrialTwin - Our Vision - Phase III | Synthetic Submission

Accelerating Analysis | Trial Designer

Integrated Simulation:

  • Build, test, and analyze each trial from one User Interface

  • Web-based tool avoids vendor lock-in, compatibility issues

  • Different roles, user access

  • Please see more details about our Trial Designer

TrialTwin - Our Vision - Accelerating Analysis | Trial Designer

ClinicalTrials.gov | Synthetic Data for 38K Open Trials

Each study includes:

  • Synthetic Subjects

  • Full medical history

  • Customized arms, visits

  • Full EDC data extract

  • All SDTM domains

  • Pre-built dashboards

  • Pre-generated reports

TrialTwin - Our Vision - ClinicalTrials.gov | Synthetic Data for 38K Open Trials

Phase IV | Post-trial Modeling

Predict operating life:

  • Use historical data to predict adverse events: device, drug

  • Simulate uploading synth trial results to ClinicalTrials.gov

TrialTwin - Our Vision - Phase IV | Post-trial Modeling

Phase V | Population Modeling

Prescription patterns:

  • Use historical data to model marketing pathways

  • (US-specific) Medicare data

TrialTwin - Our Vision - Phase V | Population Modeling

The historical data is sourced from our Regulatory Repository.

Prediction Model

In terms of direct business benefits, our prediction model could be useful to: * kill compound under study earlier * adjust clinical trial based on early results * shorten approval process * extend selling cycle * predict or prevent a drug's recall or withdrawal

TrialTwin - Prediction Model

Please see our Data Math concept.

And we presented our vision at the PHUSE EU Connect 2023 event with our Human BioGeography paper.

Contact us

Please contact us for more details.