Cover Image
QR Code

Partial Differential Equation (PDE)‐Based Spatial Pharmacometrics in NONMEM: Method of Lines (MOL) Implementation with AI‐Assisted Model Development.

Spatial heterogeneity in drug distribution, particularly within solid tumors, compromises target engagement, yet is rarely represented in population pharmacokinetic analyses. Standard "well‐stirred" models fail to capture intratumoral gradients. Reaction–diffusion partial differential equations (PDE...

Full description

Published in:Journal of Clinical Pharmacology Vol. 66; no. 6; pp. 1 - 12
Main Authors: Cheng, Yiming, Li, Yan
Format: equations & formulas tables/charts Journal Article
Published: Wiley-Blackwell Jun2026
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194810485&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 194810485
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        00912700
        5WH
      jtl: Journal of Clinical Pharmacology
      issn: 00912700
      maglogo: Y
    pubinfo:
      dt: Jun2026
      vid: 66
      iid: 6
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
    artinfo:
      ui:
        194810485
        194810485
        194810485
        10.1002/jcph.70215
        194810485
      ppf: 1
      ppct: 11
      formats:
      tig:
        atl: Partial Differential Equation (PDE)‐Based Spatial Pharmacometrics in NONMEM: Method of Lines (MOL) Implementation with AI‐Assisted Model Development.
      aug:
        au:
          Cheng, Yiming
          Li, Yan
        affil: Clinical Pharmacology and Pharmacometrics, Bristol Myers Squibb, Summit NJ, , USA
      sug:
        subj:
          Artificial Intelligence
          Models, Theoretical
          Pharmacokinetics
          Computer Simulation
          Pharmacy and Pharmacology
          Software
          Algorithms
          Bioinformatics
          Automation
      ab: Spatial heterogeneity in drug distribution, particularly within solid tumors, compromises target engagement, yet is rarely represented in population pharmacokinetic analyses. Standard "well‐stirred" models fail to capture intratumoral gradients. Reaction–diffusion partial differential equations (PDEs) mechanistically represent penetration and washout, but routine implementation in nonlinear mixed‐effects modeling (NONMEM) is limited by operational complexity. Native numerical templates remain cumbersome, and manual method of lines (MOL) coding is labor‐intensive and error‐prone. This work presents a streamlined workflow to implement spatial PDEs in NONMEM using AI tools. We utilized AI‐assisted code generation to systematically translate continuous spatial models into coupled ordinary differential equation systems directly executable in NONMEM, maintaining transparent $DES block implementations. We illustrate this approach with one‐dimensional, spherical, and two‐dimensional reaction–diffusion models, providing guidance for iterative refinement via prompt engineering. Although AI does not resolve numerical stiffness or identifiability limitations, it substantially reduces the engineering burden of large MOL systems. Coupled with disciplined verification, AI‐assisted code generation makes PDE‐based spatial pharmacometrics in NONMEM practical and maintainable, supporting wider adoption to interrogate target‐site exposure and penetration‐driven efficacy.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        tables/charts
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N