What dose and study design?
Predict human exposure, select starting dose and regimen, and optimize escalation, cohorts, biomarkers, endpoints and sampling.
Quantitative solutions for translational science
Connecting the evidence for informed decisions.
Namlink integrates human-relevant in vitro systems, prior knowledge and mechanistic models to predict first-in-human exposure, efficacy and safety—informing dose and study design while reducing reliance on in vivo studies.
NAM-first evidence system
In vitroPrior dataBiomarkersMechanism
INTEGRATEModel-Informed Drug Discovery & Development (MID3)
FIH doseEfficacySafetyStudy design
15+years in pharmaceutical development
Research → Phase 2translational strategy and execution
Regulatory-readyanalysis, reporting and interaction
What we help you decide
The questions that shape first-in-human development
Start with the decision—not the modeling method. Namlink works backward from the evidence needed to move a program forward.
Will it work in humans?
Translate target engagement, disease biology and in vitro dose–response into efficacy, biomarker and combination-therapy predictions.
What are the human safety risks?
Place in vitro hazard signals in human exposure context to predict safety margins, mechanisms, monitoring needs and risk mitigation.
How we answer it
Predict human outcomes before defaulting to animal studies
Our a priori workflow integrates new approach methodologies (NAMs) from the outset, using human-relevant biology to guide development rather than treating in vitro and in silico evidence as late-stage supplements.
- 01
Generate human-relevant inputs
Integrate biochemical and binding assays, 2D/3D cell systems, organoids, microphysiological systems, organ-on-chip data, biomarkers, omics and prior knowledge.
- 02
Build a priori models
Parameterize PBPK, QSP and QST models with drug-, target-, physiology- and disease-specific data before observing or fitting to in vivo PK, efficacy or toxicity results.
- 03
Predict human translation
Simulate human plasma and tissue exposure, target engagement, biological efficacy, toxicity mechanisms, safety thresholds and variability—with uncertainty made explicit.
- 04
Design the FIH study
Use the integrated predictions to select starting dose and regimen, escalation scheme, cohorts, biomarkers, endpoints, sampling times, monitoring and risk-mitigation criteria.
Translational toxicology
Human safety predictions in exposure context
Integrate advanced in vitro signals, biomarkers and omics with IVIVE, PBPK and QST to evaluate liver injury, marrow cytotoxicity, neuropathy and other mechanisms—then translate uncertainty into safety margins, monitoring plans and stopping criteria.
Integrated evidence to support next study designs. When in vivo evidence remains necessary, simulations focus the experiment on the unresolved gap and minimize dose groups, animals and sampling.
Modeling capabilities
The right model for the decision
MID3 provides the strategy; PBPK, QSP and QST provide complementary quantitative evidence.
01Strategy
MID3
Model-Informed Drug Discovery & Development
Connect NAM evidence and modeling activities to explicit program and regulatory questions.
- Integrated translational and modeling strategy
- FIH dose, efficacy, safety and study design
- ICH M15-aligned planning, evaluation and documentation
02Exposure
PBPK
Physiologically Based Pharmacokinetics
Translate in vitro drug properties and physiology into human plasma, tissue and site-of-action exposure.
- A priori human PK and tissue predictions
- mAb FcRn, target biology and TMDD
- FIH dose, regimen and sampling optimization
03Mechanism
QSP / QST
Quantitative Systems Pharmacology & Toxicology
Connect human disease and adverse biology to biomarkers, efficacy, toxicity and response variability.
- Disease pathways and target engagement
- Virtual populations and efficacy predictions
- Mechanistic safety and toxicity translation
Outputs
Regulatory-ready, traceable outputs and reports
Clear documentation connects every assumption, data source and model result to the question and decision it supports.
01
Question of interest, context of use and model-risk assessment
02
Analysis plans, data lineage, assumptions and reproducible model packages
03
Model evaluation, sensitivity, uncertainty and scenario summaries
04
Technical reports, briefing packages and submission-ready documentation
Therapeutic experience and credibility
Quantitative methods adapted to disease biology
ON
OncologyImmune cycle, AML, solid tumors, T-cell lymphoma, ADCs and bispecifics
IM
ImmunologyAtopic dermatitis, rheumatoid arthritis, immune pathways and anti-FcRn biology
ID
Infectious diseasesTuberculosis, site-of-action exposure and host-directed therapy
RD
Rare diseasesXLH, tumor-induced osteomalacia and gene therapy
MD
Metabolic disordersObesity, MASH/NAFLD, bone remodeling and phosphate homeostasis
Experience in practice
Grounded in real development questions
Hands-on experience across pharmacometrics, quantitative pharmacology, translational science and regulatory strategy—from research through Phase 2.
Start with the decision