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
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
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")
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
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
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
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
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
Phase V | Population Modeling
Prescription patterns:
- Use historical data to model marketing pathways
- (US-specific) Medicare data
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
Please see our Data Math concept.
And we presented our vision at the PHUSE EU Connect 2023 event with our Human BioGeography paper.