Torsten: Stan functions for pharmacometrics applications

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Transcription:

Torsten: Stan functions for pharmacometrics applications Bill Gillespie Metrum Research Group Stan for Pharmacometrics Paris, France 24 July 2018 c 2018 Torsten: Stan functions for PMX 24 July 2018 1 / 28

Torsten What is Torsten and what are we up to? What is Torsten and what are we up to? As Stan developers/collaborators we are developing Stan components for PMX applications. PMX-specific components are distributed as Torsten. More general components are contributed to Stan & Stan Math. Like Stan, Torsten is open source (BSD-3-Clause). c 2018 Torsten: Stan functions for PMX 24 July 2018 2 / 28

Torsten Teorell Torsten What is Torsten and what are we up to? T. Teorell. Kinetics of distribution of substances administered to the body. I. The extravascular modes of administration. Arch Int Pharmacodyn et Ther 57: 205-225, 1937. c 2018 Torsten: Stan functions for PMX 24 July 2018 3 / 28

Torsten PK & PKPD model functions that provide functionality similar to NONMEM s PREDPP library: Calculate state of each compartment at a sequence of event times. Event schedule specified similar to NONMEM. c 2018 Torsten: Stan functions for PMX 24 July 2018 4 / 28

Torsten PK & PKPD model functions that provide functionality similar to NONMEM s PREDPP library: Calculate state of each compartment at a sequence of event times. Event schedule specified similar to NONMEM. 1 & 2 cpt models w/ 1st order absorption solved analytically. c 2018 Torsten: Stan functions for PMX 24 July 2018 4 / 28

Torsten PK & PKPD model functions that provide functionality similar to NONMEM s PREDPP library: Calculate state of each compartment at a sequence of event times. Event schedule specified similar to NONMEM. 1 & 2 cpt models w/ 1st order absorption solved analytically. General linear model solved via matrix exponential. c 2018 Torsten: Stan functions for PMX 24 July 2018 4 / 28

Torsten PK & PKPD model functions that provide functionality similar to NONMEM s PREDPP library: Calculate state of each compartment at a sequence of event times. Event schedule specified similar to NONMEM. 1 & 2 cpt models w/ 1st order absorption solved analytically. General linear model solved via matrix exponential. General model solved numerically using Stan s built-in ODE solvers Runge-Kutta-Dopri 4th/5th order or BDF. c 2018 Torsten: Stan functions for PMX 24 July 2018 4 / 28

Torsten PK & PKPD model functions that provide functionality similar to NONMEM s PREDPP library: Calculate state of each compartment at a sequence of event times. Event schedule specified similar to NONMEM. 1 & 2 cpt models w/ 1st order absorption solved analytically. General linear model solved via matrix exponential. General model solved numerically using Stan s built-in ODE solvers Runge-Kutta-Dopri 4th/5th order or BDF. Mixed models that couple analytically solved PK models with numerically solved ODE systems. c 2018 Torsten: Stan functions for PMX 24 July 2018 4 / 28

Torsten Torsten PMX functions Use NONMEM/PREDPP conventions for data specification and event handling Data format: Time-ordered event records for each individual à la NONMEM Implemented NONMEM data types: TIME, CMT, AMT, RATE, EVID, II, ADDL, SS Recursive calculation: For each event time calculate the amount in each compartment given the compartment amounts plus doses at the previous event time. Allows for time-varying (piece-wise constant) parameter values. c 2018 Torsten: Stan functions for PMX 24 July 2018 5 / 28

Torsten Torsten PMX functions PKModelOneCpt(time, amt, rate, ii, evid, cmt, addl, ss, theta, F, tlag) PKModelTwoCpt(time, amt, rate, ii, evid, cmt, addl, ss, theta, F, tlag) linodemodel(time, amt, rate, ii, evid, cmt, addl, ss, K, F, tlag) generalodemodel *(ODE system, node, time, amt, rate, ii, evid, cmt, addl, ss, theta, F, tlag, rel tol, abs tol, max step) mixode1cptmodel *(reduced ODE system, node, time, amt, rate, ii, evid, cmt, addl, ss, theta, F, tlag, rel tol, abs tol, max step) mixode2cptmodel *(reduced ODE system, node, time, amt, rate, ii, evid, cmt, addl, ss, theta, F, tlag, rel tol, abs tol, max step) * = rk45 or bdf c 2018 Torsten: Stan functions for PMX 24 July 2018 6 / 28

Torsten Torsten: Where to get it... Torsten is available at: https://github.com/metrumresearchgroup/torsten Includes installation instructions for use with CmdStan and RStan. Documentation: https://github.com/metrumresearchgroup/ Torsten/blob/master/docs/torsten_manual.pdf Current version (0.84) uses Stan 2.17.1. We will update to Stan 2.18 soon. c 2018 Torsten: Stan functions for PMX 24 July 2018 7 / 28

Torsten Torsten example: PKPD model of drug-induced neutropenia ME-2 plasma concentrations and neutrophil counts resulting from 80 mg ME-2 q12h x 1 week ME 2 plasma concentration (ng/ml) 1500 1000 500 0 1500 1000 500 1 2 3 4 5 0 0 50 100 150 2000 50 100 150 200 time (h) 0 50 100 150 200 ANC (10 9 /L) 10 8 6 4 2 10 8 6 4 1 2 3 4 5 2 0 200 400 600 0 200 400 600 time (h) 0 200 400 600 Source code: neutropenia2.rmd Source code: neutropenia2.rmd Source graphic: /data/advancedstan/deliv/figure/neutropenia2/neutropenia2 PKPDData001.pdf Source graphic: /data/advancedstan/deliv/figure/neutropenia2/neutropenia2 PKPDData002.pdf c 2018 Torsten: Stan functions for PMX 24 July 2018 8 / 28

c 2018 Figure Torsten: 2Stan of functions reference for PMX [1] 24 July 2018 9 / 28 Torsten Friberg-Karlsson semi-mechanistic model for drug-induced myelosuppression PK model: Two compartment model with first order absorption describing plasma drug concentration on the i th occasion in the j th subject as a function of time, dose and body weight: log ( ) c ij N ( log ( ) ) ĉ ij, σ ĉ ij = f 2cpt ( tij, D j, τ j, CL j, Q j, V 1j, V 2j, k aj ) Friberg-Karlsson semi-mechanistic model for drug-induced myelosuppression [1, 2, 3, 4, 5, 6]

Torsten Friberg-Karlsson semi-mechanistic model for drug-induced myelosuppression dprol dt dtransit1 dt dtransit2 dt dtransit3 dt dcirc dt = k prol Prol (1 E drug ) = k tr Prol k tr Transit1 = k tr Transit1 k tr Transit2 = k tr Transit2 k tr Transit3 = k tr Transit3 k circ Circ E drug = αĉ ( ) γ Circ0 k tr Prol Circ ĉ plasma drug concentration Circ absolute neutrophil count (ANC) Parameters in red are system parameters, i.e., drug-independent. k prol = k circ = k tr MTT = n + 1 k tr c 2018 Torsten: Stan functions for PMX 24 July 2018 10 / 28

Torsten IIV and prior distributions Inter-individual variation log ( ) CL j, Q j, V 1j, V 2j, k aj, MTT j, Circ 0j, α j ( ( ( ) 0.75 ( ) 0.75 ( bwj N log ĈL, Q bwj, V bwj 1 70 70 70 ) ) MTT, Ĉirc 0, α, Ω ) ( ), V bwj 2, k a, 70 Prior distributions: moderately informative for PK, strongly informative for system parameters, weakly informative for drug effect ĈL log N (log(10), 0.5) Q log N (log(15), 0.5) V1 log N (log(35), 0.5) V 2 log N (log(105), 0.5) k a log N (log(2), 0.5) MTT log N (log(125), 0.2) Ĉirc 0 log N (log(5), 0.2) γ log N (log(0.17), 0.2) ( ) α log N log(3 10 4 ), 1 σ half-cauchy (0, 1) Ω = diag (ω) P diag (ω) ω i half-cauchy (0, 1), i {1, 2,..., 8} P LKJCorr (1) c 2018 Torsten: Stan functions for PMX 24 July 2018 11 / 28

Torsten Stan model excerpt with Torsten function call for(j in 1:nId){ CL[j] = theta[j, 1] * (weight[j] / 70)^0.75;. alpha[j] = theta[j, 8]; parms = {CL[j], Q[j], V1[j], V2[j], ka[j], mtt[j], circ0[j], gamma, alpha[j]}; x[start[j]:end[j],] = generalodemodel bdf(twocptneutmodelode, 8, time[start[j]:end[j]], amt[start[j]:end[j]], rate[start[j]:end[j]], ii[start[j]:end[j]], evid[start[j]:end[j]], cmt[start[j]:end[j]], addl[start[j]:end[j]], ss[start[j]:end[j]], parms, F, tlag, 1e-6, 1e-6, 1e8); chat[start[j]:end[j]] = x[start[j]:end[j], 2] / V1[j]; neuthat[start[j]:end[j]] = x[start[j]:end[j], 8] + circ0[j]; } c 2018 Torsten: Stan functions for PMX 24 July 2018 12 / 28

Torsten Good convergence and mixing with 4 chains of 250 warmup and 250 post-warmup samples/chain parameter mean SD median 95% CI Neff Rhat ĈL 11.2 1.6 11.2 (8.13, 14.7) 480 1.01 Q 22.7 4.3 22.4 (15.3, 32.7) 614 1 V 1 30.9 4.58 31 (21.5, 40.4) 701 1 V 2 117 24.5 114 (75.3, 173) 710 0.999 k a 1.78 0.293 1.77 (1.23, 2.37) 779 1.01 σ 0.1 0.00489 0.1 (0.0919, 0.111) 1000 1 α 0.000323 5.14e-05 0.000319 (0.000234, 0.000432) 792 0.998 MTT 117 10.1 116 ( 100, 141) 847 1 Ĉirc 0 5.77 0.649 5.75 (4.44, 7.05) 829 1 γ 0.152 0.0128 0.151 (0.128, 0.178) 1000 0.997 σ ANC 0.107 0.00683 0.106 (0.0951, 0.122) 1000 1 c 2018 Torsten: Stan functions for PMX 24 July 2018 13 / 28

Torsten Model fits (posterior median & 90 % CI) 1 2 3 1 2 3 ME 2 plasma concentration (ng/ml) 1500 1000 500 0 1500 1000 500 4 5 0 0 50 100 150 2000 50 100 150 200 time (h) 0 50 100 150 200 prediction individual population ANC (10 9 /L) 9 6 3 9 6 3 4 5 0 200 400 600 0 200 400 600 time (h) 0 200 400 600 prediction individual population Source code: neutropenia2.rmd Source graphic: /data/advancedstan/deliv/figure/neutropenia2/neutropenia2 PPC001.pdf Source code: neutropenia2.rmd Source graphic: /data/advancedstan/deliv/figure/neutropenia2/neutropenia2 PPC002.pdf c 2018 Torsten: Stan functions for PMX 24 July 2018 14 / 28

Torsten For more details and instruction... We provide some free online workshops at https://metrumrg.com/courses/. A Brief Introduction to Bayesian Modeling Using Stan Advanced Use of Stan, RStan and Torsten for Pharmacometric Applications Course materials including complete code and data for many illustrative examples are provided. c 2018 Torsten: Stan functions for PMX 24 July 2018 15 / 28

Torsten We re not done We want to reduce the barriers to routine use of Stan for PMX applications, e.g., Computation time required for Bayesian data analysis. Programming time required to implement typical poppkpd models. We want to expand the range of applications, e.g., models described in terms of other equation types that require numerical solution like DAEs, PDEs, SDEs, etc. c 2018 Torsten: Stan functions for PMX 24 July 2018 16 / 28

Things to come What else are we working on? c 2018 Torsten: Stan functions for PMX 24 July 2018 17 / 28

Things to come What else are we working on? What else are we working on? Additional ODE support Adams-Moulton solver for non-stiff ODEs implemented as integrate ode adams in Stan 2.18 Action of the matrix exponential function to speed up linear ODE solving implemented as matrix exp multiply and scale matrix exp multiply in Stan 2.18 DAEs Stan interface for IDAS from SUNDIALS Pull request under review PDEs Initial approach interfaces Stan with 3rd-party PDE libraries. Pull request under review R packages to simplify implementation of common classes of pharmacometrics models. for specialized visualization and reporting of PKPD model analyses. c 2018 Torsten: Stan functions for PMX 24 July 2018 18 / 28

Things to come What else are we working on? Stan interface with 3rd-party PDE libraries Interface with user-provided numerical PDE solvers. So far the interface has been tested on three external solvers. More can be added with similar fashion. OpenSees structural mechanics solver Stan Stan Math libmesh general-purpose finite element solver MFEM light-weight finite element solver... Pros Stan to a broader audience Flexibility Better modeling by field specialists Better performance tuning by field specialists... Cons Requires C++ coding and library management c 2018 Torsten: Stan functions for PMX 24 July 2018 19 / 28

0.996 1.000 1.004 1.004 1.000 0.996 0 250 500 750 1000 1 Example: Darcy s flow Things to come What else are we working on? Darcy s flow is a fundamental model describing fluid flow in many tissues. It is commonly used to model interstitial flow that plays important roles in tissue morphogenesis, function, and pathogenesis. Noisy data: u obs 2 parameter: k(ratio between viscosity and permeability) Problem is setup and solved on an arbitrary domain in MFEM. ku + p = f u = g k[1] chain u: velocity p: pressure Figure: Contour of p in PDE solution k[1] Figure: Sampling of k Figure: Trace of k c 2018 Torsten: Stan functions for PMX 24 July 2018 20 / 28

Things to come Related Stan development Related Stan development by other developers Within chain parallel computation MPI or threading approaches available in Stan 2.18. https://github.com/stan-dev/math/wiki/mpi-parallelism https://github.com/stan-dev/math/wiki/threading-support Fast approximate Bayesian engines Improvements to existing ADVI engine Gradient-based marginal optimization (GMO) for marginal maximum penalized likelihood estimation Data-streaming variational Bayes via stochastic automatic differentiation variational inference (ADVI) Data-parallel variational Bayes via expectation propagation (EP) c 2018 Torsten: Stan functions for PMX 24 July 2018 21 / 28

Our ambitions Things to come Our ambitions Where we re heading Flexible open source platform that makes BDA a practical option for routine poppk/poppkpd analyses. larger scale models (e.g., QSP models) and/or data sets. c 2018 Torsten: Stan functions for PMX 24 July 2018 22 / 28

If you re interested... If you re interested... Torsten is free and open source. c 2018 Torsten: Stan functions for PMX 24 July 2018 23 / 28

If you re interested... If you re interested... Torsten is free and open source. Kick the tires. c 2018 Torsten: Stan functions for PMX 24 July 2018 23 / 28

If you re interested... If you re interested... Take it for a spin. c 2018 Torsten: Stan functions for PMX 24 July 2018 24 / 28

If you re interested... If you re interested... Take it for a spin. Comment, recommend features, complain, etc. c 2018 Torsten: Stan functions for PMX 24 July 2018 24 / 28

If you re interested... If you re interested... Contribute code. Make it better. https://github.com/metrumresearchgroup/torsten c 2018 Torsten: Stan functions for PMX 24 July 2018 25 / 28

Acknowledgments Credit where it is due... Metrum Research Group Charles Margossian Yi Zhang Marc Gastonguay Stan developers Andrew Gelman, Columbia Univ. Bob Carpenter, Columbia Univ. Sean Talts, Columbia Univ. Mike Betancourt, Columbia Univ. Daniel Lee, Columbia Univ. Sebastian Weber, Novartis + contributions from many more Funding sources: Bill & Melinda Gates Foundation Office of Naval Research STTR Program contract no. N00014-16-P-2039 contract no. N6833518C0110 c 2018 Torsten: Stan functions for PMX 24 July 2018 26 / 28

References References I [1] L. E. Friberg, A. Henningsson, H. Maas, L. Nguyen, and M. O. Karlsson. Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. J Clin Oncol, 20(24):4713 21, 2002. [2] L. E. Friberg and M. O. Karlsson. Mechanistic models for myelosuppression. Invest New Drugs, 21(2):183 194, 2003. [3] J. E. Latz, M. O. Karlsson, J. J. Rusthoven, A. Ghosh, and R. D. Johnson. A semimechanistic-physiologic population pharmacokinetic/pharmacodynamic model for neutropenia following pemetrexed therapy. Cancer Chemotherapy and Pharmacology, 57(4):412 426, 2006. [4] I. F. Troconiz, M. J. Garrido, C. Segura, J. M. Cendros, P. Principe, C. Peraire, and R. Obach. Phase i dose-finding study and a pharmacokinetic/pharmacodynamic analysis of the neutropenic response of intravenous diflomotecan in patients with advanced malignant tumours. Cancer Chemother Pharmacol, 57(6):727 35, 2006. [5] S. J. Kathman, D. H. Williams, J. P. Hodge, and M. Dar. A bayesian population pk-pd model of ispinesib-induced myelosuppression. Clin Pharmacol Ther, 81(1):88 94, 2007. c 2018 Torsten: Stan functions for PMX 24 July 2018 27 / 28

References References II [6] Steven J Kathman, Daphne H Williams, Jeffrey P Hodge, and Mohammed Dar. A bayesian population pk-pd model for ispinesib/docetaxel combination-induced myelosuppression. Cancer Chemother Pharmacol, 63(3):469 476, 2009 Feb. c 2018 Torsten: Stan functions for PMX 24 July 2018 28 / 28

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