Translational PK/PD

Use exposure and target-engagement scenarios to support lead optimization and candidate selection

Connect compound properties, ADMET and dosing assumptions to systemic exposure, tumor exposure, target occupancy, pathway modulation and predicted efficacy.

LEAD / CANDIDATE SCENARIO COMPARISON

Lead and candidate scenario comparison chart showing exposure curves for Candidate A, B and C against target threshold
Compare Lead / Candidate ScenariosDiscuss Model Calibration

Test whether exposure and target engagement support candidate progression.

Actual Platform Interface

Review exposure, target response and emergent system behavior in one panel

Compare systemic and tumor exposure, payload kinetics, pharmacodynamic response and model-derived system metrics in a single research workflow.

Screenshot of the PK/PD platform interface showing exposure and response charts

The PK/PD plot panel links systemic and tumor ADC kinetics, payload exposure, biomarker inhibition, penetration, binding-site barriers and bystander spread.

1. Exposure and response together

Inspect circulating ADC, tumor ADC, payload and PD response on aligned timelines.

2. Mechanistic summary

Surface penetration, binding-site and bystander metrics alongside the curves.

3. Transparent numerics

Display solver, output interval, time step and data-support level for scientific review.

Mechanistic Bridge

Connect chemistry decisions to exposure and effect

  1. Compound properties
    1Compound properties
  2. ADMET
    2ADMET
  3. Systemic exposure
    3Systemic exposure
  4. Tumor exposure
    4Tumor exposure
  5. Target occupancy
    5Target occupancy
  6. Pathway effect
    6Pathway effect
  7. Efficacy scenario
    7Efficacy scenario

Candidate Scenario Comparison

Compare more than peak concentration

Candidate
Exposure
Tumor penetration
Occupancy duration
Robustness
Decision
Candidate A
AUC ↑
Moderate
18 h
Robust
Advance
Candidate B
Cmax ↑
High
8 h
Schedule-sensitive
Review
Candidate C
Low
Moderate
4 h
Clearance-sensitive
Stop

Questions The Model Should Answer

Which chemistry change is most likely to improve translational performance?

Scientists reviewing compound data together in a laboratory

Will ADMET differences change systemic or tumor exposure?

Is exposure sufficient to maintain target occupancy?

Is the candidate robust across dose and parameter uncertainty?

Which property—clearance, penetration, potency or kinetics—is the key driver?

What should influence the next medicinal-chemistry cycle?

Two Delivery Modes

Platform comparison plus project-calibrated scientific modeling

Research Use

Platform scenario modeling

Compare candidates, dose assumptions, exposure, occupancy and efficacy scenarios.

Managed Service

Project calibration

Use experimental, animal or clinical evidence to refine project-specific parameters with scientific review.

From molecular ideas to the next experimental decision

Discuss a focused project review, pilot scope or complete optimization program with MedMap.

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