AIMS Service Category

NAMs and MIDD Strategy & Execution Support

Connect NAMs and MIDD to evidence generation and interpretation strategies
for specific development questions.

AIMS defines the scope of NAMs and MIDD according to pipeline stage and decision purpose, then links nonclinical and clinical evidence quantitatively for dose, population, study design, regulatory explanation, and partner communication.

MIDD Plan PopPK Model PBPK Model ER Analysis Simulation Report
Why it matters

Pipeline knowledge built from well-combined evidence drives global development competitiveness.
In global development, the key is not isolated study results but pipeline knowledge built by combining nonclinical, clinical, and model-based evidence around the questions that matter.

Recommended for companies pursuing
global development.

This category supports clients that have or plan to generate nonclinical or early clinical data and need to connect them to dose/exposure/response prediction, NAMs applicability, MIDD planning, regulatory explanation, and partner materials.

AS IS
  • NAMs and MIDD needs are not reviewed early despite global plans.
  • Nonclinical results and clinical dose strategy are interpreted separately.
  • The timing and purpose of MIDD application are unclear.
  • NAMs are reviewed only as technologies, not as decision evidence.
  • Early clinical data are not connected to dose/regimen decisions.
TO BE
  • Purpose and scope of NAMs/MIDD are defined for the global development strategy.
  • Nonclinical and clinical evidence are linked to human dose, exposure, and response prediction.
  • Early clinical data are used to support dose/regimen optimization.
  • Modeling strategies for DDI, PBPK, QSP, and regulatory explanation are structured.
  • Quantitative evidence is connected to global partner communication and development decisions.

How NAMs and MIDD become a development strategy

AIMS starts with the pipeline development question and decision point, then selects evidence generation methods and modeling strategies to answer those questions.

MIDD planning

Plan how model-informed evidence will be used in development decisions.

01

NAMs methodology

Propose methods to complement evidence not sufficiently addressed by conventional studies.

02

Model-based translation

Connect nonclinical and clinical evidence to human prediction and dose optimization.

03

Service modules

MIDD planning, NAMs methodology, and model-based translation are combined according to development questions and decision purpose.

Pipeline-specific MIDD Planning

This service plans MIDD so it is not a retrospective analysis but a development decision tool.

Core question
How should MIDD be applied to answer this pipeline's development questions?
Why this service matters
If MIDD is considered too late, PK/PD sampling, biomarkers, covariates, and endpoints may be insufficient for actionable modeling.

What can go wrong
if this is not addressed?

01

Modeling without purpose

Analysis results may not support dose, population, or study decisions.

02

Insufficient data structure

Sampling, endpoints, covariates, or biomarkers may be inadequate.

03

Weak documentation strategy

Model results may be difficult to explain to regulators or partners.

Multidisciplinary participation

The radial chart shows participating expertise only. RACI roles and contributions are shown in the table.

MIDD Planning multidisciplinary participation graph
Accountable Responsible Consulted Informed

Role by expertise

RoleExpertiseContribution
AClinical Pharmacology

Owns the link between MIDD objectives and development decisions.

RPharmacometrics / MIDD

Plans modeling strategy, input data, timing, and simulations.

CClinical Development

Connects dose, population, endpoints, and follow-on trial design.

CNonclinical Development

Advises use of nonclinical PK/PD, efficacy, and toxicity evidence.

CRegulatory Strategy

Advises regulatory explanation and documentation strategy.

CData / Statistics

Advises data structure, covariates, missing data, and simulation inputs.

When clients need this service

Client situationAIMS value
FIH or post-FIH dose/exposure/response decisions are needed.Defines MIDD purpose and required data generation.
Early clinical data should inform later dose decisions.Plans exposure-response, simulation, and covariate analyses.
Quantitative evidence must be explained externally.Structures regulatory and partner-facing documentation.

Workflow

01

Define development questions

Clarify dose, regimen, population, and safety questions.

02

Review data status

Assess available and needed data.

03

Set MIDD scope

Define PopPK, PK/PD, PBPK, or other approaches.

04

Feed study design

Recommend sampling, endpoints, and data structure.

05

Plan result use

Define decision and documentation pathways.

Representative deliverables

Pipeline-specific MIDD plan

Questions, scope, model approach, data needs, and result use.

MIDD evidence generation plan

Study/data/analysis flow for model-informed evidence.

Modeling-ready data requirements

Sampling, endpoints, covariates, biomarkers, and data structure.

Regulatory/partner explanation logic

How MIDD outputs will be explained externally.

Relevant standards by service

AIMS defines the standards and guideline anchors that should be considered for each service, rather than treating them as optional references.

ServiceRelevant standardsHow they are applied
Pipeline-specific MIDD PlanningICH M15, E8(R1), E9(R1), M10Plan model-informed evidence, data quality, estimands, and bioanalysis requirements.
NAMs Evidence Generation StrategyICH S5, S6(R1), S7A/S7B, S12, OECD/validation principles where applicableAssess human relevance, assay quality, and evidence integration for alternative methods.
Model-based Translation StrategyICH M15, M12, E14, M10Support MIDD documentation, DDI/PBPK, QTc or concentration-QTc analysis, and bioanalytical reliability.

Core terms

NAMs

New Approach Methodologies such as organoids, in vitro/ex vivo systems, and in silico methods.

MIDD

Model-Informed Drug Development using quantitative models to inform development decisions.

PBPK

Physiologically Based Pharmacokinetic modeling.

ER analysis

Exposure-response analysis connecting drug exposure to efficacy or safety outcomes.