Product and Service

AI Helps Clinical Trials
Prediction // Business Advisory // Clinical Decision

Establish an AI clinical trial database to predict trial outcome of the applications and bio marks. Helps to reduce trial risk and trial cost.

As a powerful business development tool, AI supports evidence-based decision-making for large pharmaceutical companies for licensing opportunities and go/no-go decisions.

Currently, there is no direct competitors on the market. Big pharmas have in-house development team on their own target and candidate screenings and pre-clinical trials, whose scopes are only limited their internal programs.

Several pharmaceutical companies in China and the United States applied AI in target identification and non-clinical drug discovery, but no companies are engaged in Clinical Trials.
PD-1/PD-L1 as "Proof of Concept Plan"
step 1
Data Sources

Research Publication
Study information
Study Data
FDA Documents
step 2
Data annotations and indexes

Number of patients
Mechanism
Safety
Efficacy
Dosing/Side Effects
Biomarkers
Pharmacokinetics (PK) Pharmacodynamics (PD)
Risk-return ratio
step 3
Data Analysis

Text Mining
AI
Machine Learning
step 4
Applications

Efficacy or safety Prediction
Biomarker
Disease Symptoms Expansion
Intelligent Experiment Design
Textual evidence
Combination Strategy
New Target Selection
Competitor Intelligence
Business Development Opportunities
Due Diligence
Preclinical phase
in vitro /in vivo data on:

Biochemical/Cellular potency
Efficacy in animal model
specificity/off target activity
ADME/PK properties
preclinical safety studies
Objective:

Select FIH dose
Select tumor types related to MOA or mutation otherwise all comer
Predict efficacy and efficacious dose range
Foresee potential safety issue and implement monitoring in protocol
Estimate safety window
Phase 1/2(dose escalation/expansion/phase 2)

tolerability/safety
Early efficacy signal (usually short duration)
PK
Biomarker response
Select dose for expansion
Select indication or population for expansion phase or phase 2 if all comer in escalation phase
Phase 2 sometimes are randomized or with a control ( comparator) group
Objective:

Determine population
Dose selection
Understand efficacy/ safety and therapeutic window
Phase 3
(confirmatory study)

Efficacy ( primary endpoint), require demonstration of durability
Safety profile
usually randomized, with comparator group, blinded or open label

About Us

InfoRobot LLC is an US company based at Chadds Ford , PA. It was formed to build artificial intelligence system to help pharmaceutical industry. Overall, our company can be characterized as a startup high-tech company specializing in AI applications for oncology drug businesses. Our product line Tobi Pharma starts with clinical trial prediction systems on PD1/PD-L1 drugs.
By harnessing the power of AI technologies, our systems will learn and adapt as businesses grow. InfoRobot, LLC will be considered a vital component for developing differentiated business strategies for drug companies.

Contact Us

Inforobot LLC

1204 Baltimore Pike, Suite 301
Chadds Ford , PA 19317,USA
Tel : 001-484-639-7287
Fax: 001-610-793-1051
info@tobi.life
Frank_Yang@info-robot.com