Toward optimal moxifloxacin dosing in tuberculous meningitis: a translational physiologically based pharmacokinetic modeling approach

Toward optimal moxifloxacin dosing in tuberculous meningitis: a translational physiologically based pharmacokinetic modeling approach | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Toward optimal moxifloxacin dosing in tuberculous meningitis: a translational physiologically based pharmacokinetic modeling approach View ORCID Profile Ming Sun , Katie Lynch , View ORCID Profile Theis Mariager , View ORCID Profile Jacob Bodilsen , View ORCID Profile Roland Nau , View ORCID Profile Rob C. van Wijk , View ORCID Profile Martijn L. Manson , View ORCID Profile Elizabeth C.M. de Lange , View ORCID Profile Tingjie Guo doi: https://doi.org/10.1101/2025.11.27.25341172 Ming Sun 1 Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research (LACDR), Leiden University , Leiden, the Netherlands 6 ESCMID Study Group for Infectious Diseases of the Brain (ESGIB) , Basel, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ming Sun Katie Lynch 1 Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research (LACDR), Leiden University , Leiden, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Theis Mariager 2 Department of Infectious Diseases, Aalborg University Hospital , Aalborg, Denmark 3 Department of Clinical Medicine, Aalborg University , Aalborg, Denmark 6 ESCMID Study Group for Infectious Diseases of the Brain (ESGIB) , Basel, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Theis Mariager Jacob Bodilsen 2 Department of Infectious Diseases, Aalborg University Hospital , Aalborg, Denmark 3 Department of Clinical Medicine, Aalborg University , Aalborg, Denmark 6 ESCMID Study Group for Infectious Diseases of the Brain (ESGIB) , Basel, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jacob Bodilsen Roland Nau 4 Institute of Neuropathology, University Medical Center , Göttingen, Germany 5 Department of Geriatrics, Protestant Hospital Göttingen-Weende , Göttingen, Germany 6 ESCMID Study Group for Infectious Diseases of the Brain (ESGIB) , Basel, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Roland Nau Rob C. van Wijk 1 Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research (LACDR), Leiden University , Leiden, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rob C. van Wijk Martijn L. Manson 1 Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research (LACDR), Leiden University , Leiden, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martijn L. Manson Elizabeth C.M. de Lange 1 Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research (LACDR), Leiden University , Leiden, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elizabeth C.M. de Lange Tingjie Guo 1 Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research (LACDR), Leiden University , Leiden, the Netherlands 6 ESCMID Study Group for Infectious Diseases of the Brain (ESGIB) , Basel, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tingjie Guo For correspondence: t.guo{at}lacdr.leidenuniv.nl Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background: Tuberculous meningitis (TBM) is a severe central nervous system (CNS) infection with high mortality. Moxifloxacin shows potent anti-mycobacterial activity and favorable CNS penetration. However, optimal dosing remains uncertain, particularly with rifampicin, which markedly reduces moxifloxacin exposure. This study aimed to determine optimal moxifloxacin dosing for TBM by accounting for regional CNS pharmacokinetics (PK) and rifampicin co-administration using a cross-species translational physiologically based pharmacokinetic (PBPK) model. Methods A PBPK model was developed using high-resolution plasma and CNS microdialysis data from pigs to capture CNS physiology and moxifloxacin distribution. The model was translated to humans using literature-derived physiological parameters and allometric scaling and validated with plasma and CNS PK data from healthy subjects and TBM patients. Simulations of once-daily moxifloxacin doses (400 to 1000 mg), with and without rifampicin, were evaluated using the unbound fAUC0-24/MIC target of 53. Results The model accurately reproduced observed moxifloxacin concentrations in porcine and human CNS compartments. Simulations showed regional PK differences, with highest concentrations in the subarachnoid space and cisterna magna and lowest in brain extracellular fluid. Rifampicin reduced mean CNS exposure by 26%. Without rifampicin, target attainment was achieved at 800 mg once daily, while no simulated regimen reached target levels with rifampicin co-administration. Conclusion These results suggest that an 800 mg once-daily moxifloxacin regimen may provide adequate CNS exposure in TBM patients not receiving rifampicin. For patients treated with rifampicin, a higher dose might be necessary to achieve therapeutic targets. 1 Introduction Tuberculous meningitis (TBM) is the most severe manifestation of central nervous system (CNS) tuberculosis (TB), associated with high mortality and long-term neurological disability despite treatment ( 1 , 2 ). The World Health Organization (WHO) recommends an initial four-drug regimen consisting of isoniazid, rifampicin, pyrazinamide, and ethambutol, but its efficacy is increasingly undermined by multidrug resistance and treatment-related adverse effects ( 3 , 4 ). Recent efforts on investigating intensified rifampicin dosing or adjunctive linezolid have shown limited success in improving TBM outcomes ( 5 , 6 ). Moxifloxacin, a fourth-generation fluoroquinolone, has potent bactericidal activity against Mycobacterium tuberculosis (Mtb) ( 7 , 8 ) and favorable CNS penetration ( 9 ), making it a promising adjunct or alternative in TBM regimen, particularly for drug-resistant or refractory cases ( 10 ). It was also essential for the pulmonary tuberculosis treatment shortening from 6 to 3 months together with rifapentine ( 11 ). Since rifampicin is the cornerstone drug that commonly used in the combination therapy, it can interact with other drugs such as moxifloxacin by inducing phase II drug-metabolizing enzymes to reduce their systemic concentrations ( 12 ). This drug-drug interaction complicates optimization of combination regimens and underscores the need for a deeper understanding of moxifloxacin pharmacokinetics (PK) in the CNS during rifampicin co-administration. Available human PK data are limited to sparse cerebrospinal fluid (CSF)-to-plasma ratios or isolated case reports, which do not reliably reflect drug concentrations at key CNS sites involved in TBM pathogenesis ( 4 , 13 – 15 ). Mtb is believed to first cross the blood-brain barrier (BBB), reside the parenchyma (forming Rich foci), and later enter into CSF compartments, particularly the subarachnoid space (SAS), eventually causing inflammation ( 16 , 17 ). Thus, understanding regional CNS PK exposure beyond CSF alone is critical to improve the current treatment. Animal models remain essential for studying CNS PK to overcome the limitations of direct human sampling. However, translating findings from animal to human requires a mechanistic approach that accounts for interspecies physiological differences. Physiologically based pharmacokinetic (PBPK) modeling provides a robust framework for achieving this goal. Such models incorporate species-specific anatomy and physiology to simulate regional drug distribution, with human parameters obtained from literature or derived via allometric scaling ( 18 ) ( 19 ). By further integrating TBM pathology, CNS-focused PBPK models are capable of mechanistically predicting drug exposure across relevant CNS compartment in TBM patients, including brain extracellular fluid and multiple CSF compartments( 16 , 17 ). To date, no study has applied such a framework to moxifloxacin CNS distribution in TBM patients. In this study, we aimed to investigate optimal moxifloxacin treatment for TBM by mechanistically characterizing its regional CNS distribution with and without rifampicin using a translational PBPK modeling approach informed by detailed porcine data. 2 Method 2.1 Data Sources 2.1.1 Porcine PK Data Preclinical data from a porcine model ( 20 ) served as the foundation for developing the CNS PBPK model. The study included six female pigs (Danish Landrace breed, 3 months old, weighing 38–48 kg) receiving a single moxifloxacin intravenous (IV) dose of 6 mg/kg. Unbound moxifloxacin concentrations were obtained from plasma and multiple CNS regions including brain extracellular fluid (brainECF), brain ventricles (VENs), cisterna magna (CM), and lumbar site using microdialysis. Extensive samples were collected at 40, 80, 120, 160, 200, 240, 300, 360, 420, and 480 minutes post-dose. 2.1.2 Human PK Data - Non-inflamed Meninges PK data from a published study involving patients with non-inflamed meninges undergoing urological surgery ( 21 ) were extracted and used to evaluate the translational performance from pigs to humans of the developed PBPK model. In this study, 50 patients received a single oral dose of 400 mg moxifloxacin prior to surgery. Moxifloxacin concentrations were measured in both plasma and lumbar CSF, with patients divided into five groups (n=10) according to predefined sampling intervals: group I (0.5–1 h), group II (1–2 h), group III (2–4 h), group IV (4–6 h), and group V (6–8 h). These data provided reference concentrations for model validation in the absence of CNS inflammation. 2.1.3 Human PK Data - TBM Patients PK data from six studies involving patients with tuberculous meningitis (TBM) were extracted and used to validate the predictive performance of the PBPK model under TBM conditions in humans ( Table S1 ). These studies provided clinical data on moxifloxacin concentrations in plasma and CSF of TBM patients, which was used to evaluate the model’s applicability in the disease condition ( 4 , 14 , 15 , 22 – 24 ). 2.2 CNS PBPK model development and translation 2.2.1 Porcine CNS PBPK Model Plasma PK was modeled using an empirical compartmental model structure. One-, two-, and three-compartment plasma models were evaluated, and visual inspection of the fitted concentration–time profiles indicated that the two-compartment model provided the best agreement with the observed plasma data. A CNS PBPK model was then constructed to describe moxifloxacin distribution in pigs across CNS compartments. The CNS PBPK model structure included brain ECF, brain ventricles, cisterna magna, and SAS, with drug exchange governed by permeability and CSF flow parameters ( Figure 1 ). Pig-specific CNS physiological parameters were sourced from literature where possible ( Table S2 ), with any missing values estimated through model fitting to the porcine PK dataset ( Table S3 ). Download figure Open in new tab Figure 1: Translational PBPK modeling workflow and CNS model structure for moxifloxacin. (A) Model development and translation of the moxifloxacin PBPK model. A healthy-pig PBPK model was built with systemic circulation PK parameters (the absorption rate constant (K a ), central compartment volume (V CEN ), peripheral volume (V PER ), systemic clearance (CL e ), inter-compartmental clearance (Q), estimated CNS barrier & CNS flow related parameters (CL 12 , CL 21 , CL 13 , CL 31 , QECF, QCSF VEN , QCSF DIAS , QCSF SYS , and QCSF REB ), and fixed physiological volumes (VECF, VBVEN, VCM, VSAS) of CNS compartments. For human translation, pig physiological parameters were replaced by literature-reported human value where available, while unmeasured parameters (including BBB/BCSFB barrier and CSF related parameters) were allometrically scaled; empirical PK parameters were then re-estimated from plasma profiles of non-meningitis subjects, and the resulting model were validated against subarachnoid-space (SAS) concentrations. The healthy-human model was then adapted to tuberculous meningitis (TBM) b replacing plasma model parameters under healthy condition with diseased values and was validated against drug plasma and SAS concentrations of TBM patients, yielding a disease-specific PBPK model. (B) Schematic of the CNS PBPK submodel structures. A central (plasma) compartment (V CEN ) is linked either to a peripheral tissue compartment (V PER , Q) or to an absorption depot (k a , t lag ), with elimination CL e . Drug distributes between plasma and brain extracellular fluid (ECF; V ECF ) via barrier clearances (CL 12 , CL 21 , CL 13 , CL 31 ). From ECF, moxifloxacin can cross three serial CSF compartments i.e. brain ventricles (V BVEN ), cisterna magna (V CM ) and SAS (V SAS ) driven by unidirectional flows (QECF, QCSF VEN ) and bidirectional CSF flows (QCSF DIAS , QCSF SYS ) with recirculation (QCSF REB ) back to plasma. Arrows indicate the direction of drug transport. 2.2.2 Human CNS PBPK Model In order to translate the porcine model to human, species-specific physiological parameters (e.g., brain volume, surface area, CSF volume) of the developed porcine PBPK model were replaced with human values obtained from literatures. BBB/BCSFB-related parameters (CL 12 , CL 21 , CL 13 , CL 31 ), and CNS flow parameters (QECF, QCSF VEN , QCSF DIAS , QCSF SYS , QCSF REB ), were scaled using allometric methods ( Equation 1 and 2 ), due to the absence of direct human literature values ( Figure 1 ): Where SA BBB/BCSFB stands for the surface area of the BBB or blood-CSF barrier (BCSFB). Typical values of SA BBB/BCSFB for pigs and humans ( Table S4 ) were used to calculate surface area ratios. BBB/BCSFB-crossing related parameters were scaled using Equation 1 based on the surface area ratio, given the conserved expression levels of most active transporters in the plasma membrane fractions of the BBB and BCSFB between pig and human ( 25 – 27 ). Where Vbrh/brp stands for the brain volume of human or pig. CNS flow-related parameters were scaled from pig to human using Equation 2 based on human-to-pig brain volume (V br ) ratio using an average exponent of 0.95 from a previous cross-species cortical blood flow scaling practices ( 28 ). Using an exponent greater than the conventional 0.75 reflects the brain’s disproportionately high metabolic rate compared to whole-body processes ( 29 ). Physiological parameters such as brain weight, volume, and surface area were fixed using published human physiological values ( Table S4 ). Plasma PK parameters, including lag time (t lag ), K a , V CEN , and CL e ( Table S3 ), were determined by fitting published mean plasma concentration data from human subjects without meningitis ( 21 ) using a one-compartment model. The plasma unbound fraction (f u ) was fixed as a reported value of 50% ( 30 , 31 ). 2.2.3 Model Adaptation for TBM Conditions CNS related parameters, including CL 12 , CL 21 , CL 13 , CL 31 , were assumed to be identical between TBM patients and non-TBM patients, given moxifloxacin’s favorable CNS penetration under non-inflamed conditions and the likely minimal increase in CNS clearance associated with inflammation ( 21 ). Moreover, there were no quantitative data available to justify adjusting the other CNS related parameters (QECF, QCSF VEN , QCSF DIAS , QCSF SYS , and QCSF REB ). To account for the effect of rifampicin on moxifloxacin plasma clearance ( 32 ) as well as the effect of systemic infection, we collected plasma PK parameters for both moxifloxacin alone and in combination with rifampicin ( Table S3 ) from the tuberculosis patients ( 33 ). 2.3 CNS PBPK Model Diagnostics and Validation 2.3.1 Pig model diagnostics Model performance in pigs was assessed by comparing predicted against observed concentration-time profiles in all compartments. Goodness-of-fit plots and visual predictive check (VPC) ( 34 ) were used to evaluate prediction accuracy at both population and individual levels. Residual scatter plots were used to assess model bias. 2.3.2 Validation in Humans with Non-inflamed Meninges Model predictions of moxifloxacin concentrations in the human SAS were evaluated using root mean square error (RMSE) to quantify accuracy against observed data from non-meningitis patients. To assess the robustness of our translational approach, we additionally compared our predictions to those generated using alternative allometric scaling methods for barrier permeability and CSF flow parameters ( Table S4 ). 2.3.3 Validation in TBM Patients The adapted TBM model was validated by comparing predicted PK metrics including the area under the concentration–time curve over 24□hours (AUC 0-24 ), maximum concentration (C max ), half-life (t 1/2 ) and full concentration-time profiles, against observed data from published TBM studies (Table 1). These comparisons assessed the model’s ability to capture moxifloxacin behavior in the presence of disease-related drug CNS PK alteration. 2.4 Simulation Studies To assess regional CNS PK in comparison to plasma, moxifloxacin steady-state concentrations were simulated for a standard oral dosing regimen of 400□mg once daily across multiple CNS compartments: plasma, brain ECF, brain VENs, CM, and SAS. To assess moxifloxacin efficacy against Mtb, the ratio of the AUC 0-24 to the minimal inhibitory concentration (MIC) was applied as a predictive index, with thresholds of at least 100 based on total drug concentrations ( 35 ) and 53 for unbound concentrations ( 36 ). The unbound AUC 0-24 was simulated at steady state following oral doses of 400 mg, 600 mg, 800 mg, and 1000 mg once daily across all the compartments. The clinical breakpoint MIC for Mtb was set at 0.5 mg/L ( 37 ). The resulting unbound AUC 0-24 /MIC ratios for each compartment were then compared against the efficacy threshold of 53. All simulations were conducted considering two scenarios, with and without rifampicin co-administration. 2.5 Software and Tools The observed concentration data were extracted using WebPlotDigitizer version 4.2. Parameter estimations were performed in Monolix 2024R1( 38 ). Data simulations were conducted by the R package rxode2 version 4.1.0 ( 39 ). All visualizations, including concentration-time profiles, goodness-of-fit plots, and residual scatter plots, were generated using the R package ggplot2 version 3.5.1. 3 Results 3.1 Model Performance in Pigs The porcine CNS PBPK model accurately captured moxifloxacin concentration-time profiles across plasma and CNS compartments. Predicted median concentrations and 90% prediction intervals aligned closely with observed data from six individual pigs ( Figure 2 ). Goodness-of-fit plots at both individual and population levels showed overall agreement between observed and predicted values, however, observed concentrations from pig ID3 in the brainVENs, CM, and SAS compartments were higher than predicted at population levels ( Figure S1 ). Individual fits confirmed accurate tracking of observed concentrations across compartments ( Figure S2 ). Visual predictive checks ( Figure S3 ) indicated good model performance, although residual diagnostics ( Figure S4 ) revealed a time-dependent pattern in plasma, brainECF, and CM, suggesting mild residual error drift over the sampling interval. Overall, these findings support the reliability of the porcine model in characterizing moxifloxacin CNS distribution. Download figure Open in new tab Figure 2: Model-predicted moxifloxacin concentration-time profiles in plasma, brain extracellular fluid (ECF), brain VENs (ventricles), cisterna magna (CM), and subarachnoid space (SAS) in pigs. Observed data from six pigs ( 20 ) (colored points) and 90% prediction intervals (shaded regions) were shown on log Y scale. 3.2 Model Validation in Humans with Non-inflamed Meninges The translated human CNS model showed good predictive accuracy in non-meningitis patients receiving moxifloxacin monotherapy. Predicted moxifloxacin concentrations in the SAS were consistent with observed data across sampling intervals ( Figure 3 ). When compared to other published scaling methods ( Table S4 ), our approach yielded the closest match to clinical observations, whereas alternative scaling methods produced larger deviations (RMSE 0.97 vs. RMSE 1.2-1.5; Figure S5 ), supporting the suitability of our scaling strategy. Download figure Open in new tab Figure 3: Comparison of predicted and observed moxifloxacin concentrations in the subarachnoid space (SAS) of human subjects without meningitis. Model predictions are shown as solid black line. Observed human data ( 21 ) were represented as pink circles, with vertical (red) and horizontal (blue dashed) error bars indicating standard deviations and sampling-time intervals, respectively. 3.3 Model Validation in TBM Patients The adapted TBM model successfully reproduced observed plasma and SAS concentration-time profiles from multiple clinical studies ( Figure 4 ). Predicted plasma AUC 0-24 , C max , and t□/□ values were consistent with observed medians for both 400□mg and 800□mg dosing scenarios ( Figure 4A ). While the predicted AUC 0-24 and C max values increased proportionally with dose, the observed data indicated a degree of nonlinearity between the two dosing levels ( 4 ). Across TBM studies, most observations, except for lower values reported from one case study ( 24 ), fell within ±2-fold of the model predictions ( Figure 4B ), suggesting a good model predictive performance. Download figure Open in new tab Figure 4: Validation of human CNS PBPK model predictions for moxifloxacin in TBM patients co-administered with rifampicin. A. Boxplots of model-predicted pharmacokinetic parameters—area under the concentration–time curve from 0 to 24 hours (AUC 0-24 ), maximum concentration (C max ), and half-life (t 1/2 )—in plasma (top row) and subarachnoid space (SAS; bottom row) at 400□mg and 800□mg doses. Observed reference values ( 4 ) are depicted as horizontal lines (median) and shading (interquartile range, 25th–75th percentile) within each boxplot; predicted values are shown as circles. B. Model-predicted typical concentration–time profiles (black lines) in plasma and SAS compared with observed clinical data (colored dots) from multiple TBM studies ( 14 , 15 , 22 – 24 ). Dashed grey lines represent a two-fold error range around the model-predicted curves and the y axis is logarithmic. The observed concentrations of both plasma and SAS were considerably lower from one case report ( 24 ) than the other TBM studies. 3.4 Comparative PK/PD Analysis of Moxifloxacin in Plasma and Key CNS Compartments Simulated steady-state PK profiles revealed regional heterogeneity in moxifloxacin exposure across CNS compartments ( Figure 5A ). Concentrations were highest in the SAS, followed by the CM and brain VENs, and lowest in the brain ECF. Coadministration of rifampicin reduced exposures in plasma and all CNS compartments. Regarding the drug efficacy evaluation using an fAUC 0-24 /MIC threshold of 53 ( Figure 5B ), the 400 and 600 mg once-daily regimens did not achieve the target in most CNS compartments, whereas 800 and 1000 mg generally did when moxifloxacin was administered alone. In combination with rifampicin, only the 1000 mg regimen attained the target in most compartments except for brain ECF that remained sub-target (fAUC 0-24 /MIC ≈ 42). Download figure Open in new tab Figure 5: Moxifloxacin (MFX) pharmacokinetic (PK) profiles and target attainment with and without rifampicin (RIF). A. Simulated steady-state concentration-time profiles of MFX under standard dosing (400 mg oral once daily) in plasma and key CNS compartments: brain extracellular fluid (Brain ECF), brain ventricles (Brain VENs), cisterna magna (CM), and subarachnoid space (SAS). Left: MFX alone; right: MFX + RIF. Colors denoted compartments. B. Bar plots of the ratio of the 24-hour area under the concentration–time to the minimal inhibitory concentration (fAUC 0–24 /MIC) based on 400, 600, 800, and 1000 mg once per day of MFX (rows) with or without RIF (columns). The dashed horizontal line at 53 marked the fAUC 0-24 /MIC target ( 36 ). 4 Discussion Current understanding of moxifloxacin PK in the human CNS remains limited to sparse CSF data, providing little insight into regional exposure or the impact of rifampicin co-administration. This study addressed these gaps by quantitatively describing moxifloxacin CNS distribution leveraging a cross-species translational PBPK modeling approach. Simulations showed regional concentration differences in the CNS compartments (SAS > CM > brain VENs > brain ECF). Co-administration with rifampicin substantially reduced moxifloxacin exposure in plasma and, consequently, across CNS compartments. Relative to the PK/PD target fAUC□–□□/MIC of 53, higher moxifloxacin doses were predicted to improve target attainment in key CNS sites particularly when combined with rifampicin in TBM. We found that estimated moxifloxacin drug concentrations differed significantly across human CNS compartments. To date, human moxifloxacin CNS samples have only been obtained from SAS. For comparison, only a few other drugs have been systematically studied for their CNS PK exposure in humans, and the corresponding data align with our simulated findings of CNS regional heterogeneity ( 40 – 43 ). Even lipophilic agents such as linezolid and metronidazole were not homogenously distributed within the CNS, with higher concentrations in brain VENs than in brain ECF ( 40 , 41 ), while the SAS was consistently the compartment with highest concentrations ( 42 , 43 ). Our simulations also indicated high BCSFB penetration of moxifloxacin with an unbound AUC ratio (brain VENs/plasma) approaching 1. Similar values have been observed for ofloxacin, which has similar chemical structure with moxifloxacin, under both inflamed and non-inflamed meningeal conditions ( 44 , 45 ). Therefore, plasma or lumbar CSF samples may overestimate therapeutic coverage in other sites such as brain ECF. This regional heterogeneity in CNS drug exposure has direct implications for dose optimization, as therapeutic success in TBM depends mainly on achieving adequate drug levels at the relevant CNS sites of infection. In a randomized trial in TBM with limited patients size (n=19), regimens including higher-dose moxifloxacin (800 mg) did not improve clinical outcomes versus standard 400 mg ( 46 ). Our simulation results provide a mechanistic insight: under rifampicin co-administration, CNS exposures fall proportionally with the systemic decrease, leaving several infected regions below PK/PD target at 400-800 mg. Rifampicin, a broad inducer of drug-metabolizing enzymes/transporters ( 47 ), reduced moxifloxacin systemic AUC 0-24 by ∼31% ( 12 ) while preserving dose-proportional PK in plasma and CSF from 400 to 800 mg ( 46 ). We assumed rifampicin’s impact on moxifloxacin’s PK as a systemic effect, since there is no human evidence that rifampicin meaningfully alters moxifloxacin CNS PK via local induction on enzymes or transporters at the BBB/BCSFB/brain. The expression levels of uridine diphosphate glucuronosyltransferase and sulphotransferase, which play important roles in moxifloxacin disposition ( 48 ), are markedly lower in the CNS barriers compared to that in liver ( 49 , 50 ). Thus, rifampicin likely has a minimal or no effect on CNS-specific disposition of moxifloxacin, and CNS exposures fall largely in proportion to the systemic decrease. Our results emphasize reassessing moxifloxacin dosing strategies in rifampicin-containing TBM regimens, where reduced CNS exposure may compromise efficacy especially in patients with brain ECF involvement. Nonetheless, while the results suggested that doses > 1000 mg may be required to meet moxifloxacin PK/PD target fAUC□–□□/MIC > 53 across all infected CNS sites during rifampicin concomitant treatment, extrapolation above 800 mg should be cautious because non-linearities have been observed from 400 to 800 mg in TBM, e.g., CSF:plasma AUC ratios decreased at 800 mg in clinical sampling ( 4 ). Additionally, the current study focused on the target attainment of moxifloxacin alone, while rifampicin also contributes bactericidal activity within the CNS. Rifampicin’s direct effect on bacterial killing or and potential pharmacodynamic interactions between the two agents warrant further evaluation, but these aspects could not be assessed in the current study ( 69 ). Meanwhile, a large-scale clinical study has been initiated to continue assessing the therapeutic value of intensified rifampicin and moxifloxacin dosing in TBM ( 51 ). As TBM pathogenesis likely progresses from brain ECF to ventricular CSF and eventually to the SAS ( 52 ), maintaining adequate drug levels throughout this pathway is critical. Disease severity may shift bacillary burden toward the SAS in later stages ( 17 ), suggesting the need to map stage-dependent target sites to optimize dosing. Our model was designed to mechanistically characterize moxifloxacin disposition in the CNS while representing systemic PK by a one-compartment model. For trans-barrier drug movement across the BBB/BCSFB, we directly estimated the drug permeability rates from the pig concentration-time data, rather than partitioning into passive and active components due to limited mechanistic data existing for active moxifloxacin transport at the BBB. Regarding the CSF turnover, classical CNS PBPK models ( 53 , 54 ) held the historical assumption ( 55 ) that constant, unidirectional CSF circulation equal to choroid-plexus secretion rate across all CSF spaces and returning from SAS to the blood. We instead implemented bidirectional CSF dynamics between CM and SAS with opposing flows, QCSF DIAS (towards head) and QCSF SYS (away from head), better reflecting MRI-observed bidirectional CSF dynamics ( 56 , 57 ) and therefore providing a more physiologically accurate representation of CNS drug transport ( 58 ). We also estimated a unidirectional CSF reabsorption rate from SAS into plasma rather than fixing it to choroid-plexus production rate since the current evidence ( 59 – 61 ) suggested choroid-plexus secretion alone does not account for total CSF turnover. Given no direct human measurements for the CNS-specific parameters (QECF, QCSF VENs , QCSF DIAS , QCSF SYS , QCSF REB ), we used CNS-relevant interspecies scaling, including human-to-pig surface area ratios for BBB and BCSFB permeability parameter and brain volume ratio for CNS flow rates. The classical allometry (Kleiber’s law) uses the body mass ratio as scaling base and is commonly used to predict the systemic parameter such as plasma clearance rate, but might not be suitable for CNS specific parameters ( 62 ). Our scaling approach showed better predictive accuracy ( Figure S5 ) compared to the classical scaling, suggesting that CNS-relevant scaling ratios may be prioritized over default body mass when translating CNS parameters. Because mammalian brain density averages ∼1 g/mL, brain volume and weight could be effectively interchangeable as scaling bases in the practice ( 63 – 65 ). Moreover, higher exponent of 0.95 for the CNS parameter scaling might be needed since the classical 0.75 exponent represents basal metabolic rate and may not reflect relatively high brain metabolic demands ( 66 ). It is worth noting that the presented modeling approach did not incorporate the mechanisms such as enzyme or transporter-mediated drug disposition that could cause non-linear PK and thus exhibited dose-proportional (linear) moxifloxacin kinetics. This assumption appears reasonable, as clinical data reported linear PK across the tested range of at least 50–800 mg ( 67 ). The model further assumed uniform CNS physiology and BBB/BCSFB barrier integrity since moxifloxacin CNS penetration is generally favorable even under non-inflamed conditions ( 21 ). However, TBM may produce highly variable and localized inflammation that affects drug penetration and limits CSF flow rates in case of obstructive hydrocephalus ( 68 ), highlighting the need for additional data to quantify these effects. Moreover, clinical validation relied on sparse and heterogeneous PK data from case reports and small cohorts, which limits the precision of model verification. Incorporating patient-level variability, disease staging, and longitudinal data would strengthen predictive accuracy. Comprehensive CNS PK data are also required to perform PK/PD analyses, especially to calculate the f AUC 0-24 /MIC, the key predictor of fluoroquinolone efficacy in CNS infections. Finally, It should be noted that the commonly used f AUC 0-24 /MIC threshold of 53 was derived using the plasma PK data ( 36 ), while there is currently no validated CNS-specific cutoff exists for TBM. Deriving CNS-specific PK/PD target is therefore warranted for the future efforts, and possibly linking PK/PD to clinical outcomes as well. ( 69 ) Conclusion In conclusion, we used a translational cross-species PBPK modeling approach to characterize regional CNS moxifloxacin exposure and to inform optimal dosing strategies. We found that higher dose might be needed to ensure adequate therapeutic coverage in TBM when moxifloxacin is combined with rifampicin. Our findings underscore the need to consider regional CNS differences and drug-drug interactions particularly with rifampicin when designing effective treatment strategies. Data Availability All data produced in the present study are available upon reasonable request to the authors Supplementary document View this table: View inline View popup Table S1. Study characteristics of moxifloxacin pharmacokinetics in tuberculosis meningitis View this table: View inline View popup Download powerpoint Table S2. Physiological parameters (fixed value in the model) View this table: View inline View popup Table S3. The remaining input parameters of moxifloxacin in healthy pig, healthy and disease human models View this table: View inline View popup Download powerpoint Table S4. Scaling methods used for translating related parameters from pig to human in healthy conditions Download figure Open in new tab Figure S1: Goodness-of-fit analysis for individual and population predictions. Goodness-of-fit plots comparing observed versus predicted moxifloxacin concentrations for individual (top row) and population (bottom row) predictions in plasma, brain extracellular fluid (ECF), brain ventricles (VENs), cisterna magna (CM), and subarachnoid space (SAS). The red dashed lines represent the line of unity, indicating perfect agreement between observed and predicted values. And the spline shows the trend in predictions. Download figure Open in new tab Figure S2: Individual model fitting of moxifloxacin concentration-time profiles. Model fit for each individual pig (ID 1–6) across different compartments [plasma, brain extracellular fluid (ECF), brain ventricles (VENs), cisterna magna (CM), and subarachnoid space (SAS)]. Blue circles indicate observed data points, while the magenta lines represent the model-predicted concentration-time profiles for each individual. Download figure Open in new tab Figure S3: Visual predictive check (VPC) of moxifloxacin concentrations. Visual predictive check (VPC) for moxifloxacin concentrations in plasma, brain extracellular fluid (ECF), brain ventricles (VENs), cisterna magna (CM), and subarachnoid space (SAS). Solid lines represent the median and 90% prediction intervals of the model, while the shaded areas correspond to the 90% confidence intervals of the predicted data. The black dots represent observed data from individual pigs. Download figure Open in new tab Figure S4: Residual analysis: IWRES and PWRES plots. Individual weighted residuals (IWRES) versus model predictions and time (Upper two panels) and population weighted residuals (PWRES) versus model predictions and time (Lower two panels) for moxifloxacin concentrations in plasma, brain extracellular fluid (ECF), brain ventricles (VENs), cisterna magna (CM), and subarachnoid space (SAS). The dashed lines represent the 95% confidence limits for the residuals, and the orange line shows the trend in the residual distribution. 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