1 24 September 2015 2 EMA/CHMP/594085/2015 3 Committee for Human Medicinal Products (CHMP) 4 Guideline on the use of pharmacokinetics and 5 pharmacodynamics in the development of antibacterial 6 medicinal products 7 8 Draft 9 Draft agreed by Infectious Diseases Working Party (IDWP) May 2015 Adopted by CHMP for release for consultation 24 September 2015 Start of public consultation 28 September 2015 End of consultation (deadline for comments) 31 March 2016 10 11 This guideline replaces Points to Consider on Pharmacokinetics and Pharmacodynamics in the 12 Development of Antibacterial Medicinal Products (CHMP/EWP/2655/99) 13 Comments should be provided using this template. The completed comments form should be sent to [email protected] 14 Keywords Epidemiologic cut-off value; Exposure-response relationship; Minimal inhibitory concentration; Pharmacodynamics; Pharmacokinetics; Pharmacometrics; Pharmacokinetic-pharmacodynamic index, magnitude and target; Probability of target attainment; Wild-type distribution 15 30 Churchill Place ● Canary Wharf ● London E14 5EU ● United Kingdom Telephone +44 (0)20 3660 6000 Facsimile +44 (0)20 3660 5555 Send a question via our website www.ema.europa.eu/contact An agency of the European Union © European Medicines Agency, 2015. Reproduction is authorised provided the source is acknowledged. Guideline on the use of pharmacokinetics and 16 pharmacodynamics in the development of antibacterial 17 medicinal products 18 19 TABLE OF CONTENTS 20 EXECUTIVE SUMMARY ................................................................................. 3 21 1. INTRODUCTION(BACKGROUND) .............................................................. 4 22 2. SCOPE ...................................................................................................... 4 23 3. LEGAL BASIS ........................................................................................... 5 24 4. MAIN GUIDELINE TEXT ............................................................................ 6 25 4.1. MICROBIOLOGICAL DATA ........................................................................................................ 6 26 4.2. DETERMINING PK-PD INDICES AND PK-PD TARGETS ....................................................................... 7 27 4.2.1. Introduction ............................................................................................................. 7 28 4.2.2. Nonclinical PK-PD studies ........................................................................................... 8 29 4.2.3. Analyses of PK-PD relationships .................................................................................. 9 30 4.3. CLINICAL PHARMACOKINETIC DATA TO SUPPORT PK-PD ANALYSES ........................................................ 9 31 4.3.1. PK data from uninfected subjects ................................................................................ 9 32 4.3.2. PK data from infected patients .................................................................................. 10 33 4.3.3. Other relevant data ................................................................................................. 10 34 4.4. DETERMINATION OF THE PROBABILITY OF TARGET ATTAINMENT (PTA)................................................... 11 35 4.4.1. Use of simulations ................................................................................................... 11 36 4.4.2. Probability of target attainment (PTA) ....................................................................... 12 37 4.5. CLINICAL EXPOSURE-RESPONSE (E-R) RELATIONSHIPS ................................................................... 13 38 4.5.1. Potential value of E-R relationships ........................................................................... 13 39 4.5.2. Analyses of E-R relationships .................................................................................... 14 40 4.5.3. Applications of E-R relationships ............................................................................... 14 41 4.6. IDENTIFICATION OF BETA-LACTAMASE INHIBITOR DOSE REGIMENS ....................................................... 15 42 4.6.1. Considerations for identifying dose regimens .............................................................. 15 43 4.6.2. Approaches to identifying BLI dose regimens .............................................................. 15 44 4.6.3. Additional analyses to assess the BLI dose regimen .................................................... 16 45 4.7. REGULATORY IMPLICATIONS................................................................................................... 16 46 DEFINITIONS ............................................................................................ 18 47 REFERENCES .............................................................................................. 19 48 49 Guideline on the use of pharmacokinetics and pharmacodynamics in the development of antibacterial medicinal products EMA/CHMP/594085/2015 Page 2/21 50 EXECUTIVE SUMMARY 51 This Guideline replaces the Points to consider on pharmacokinetics and pharmacodynamics in the 52 development of antibacterial medicinal products (CPMP/EWP/2655/99). The Guideline has been 53 developed to outline the regulatory expectations for application dossiers and reflects both the scientific 54 advances in the field of pharmacometrics that have implications for antimicrobial agent development 55 programmes and the regulatory experience since the adoption of CPMP/EWP/2655/99. In a field that is 56 continually advancing the Guideline does not attempt to provide detailed recommendations on issues 57 such as methodologies for modelling and simulation. In addition, the Guideline does not specifically 58 address the use of pharmacometrics to identify susceptibility testing interpretive criteria. Sponsors are 59 encouraged to discuss the use of pharmacokinetic-pharmacodynamic (PK-PD) analyses to support the 60 development of new antimicrobial agents and when planning to add to or amend the dose 61 recommendations for licensed agents with EU Regulators. 62 Before embarking on PK-PD analyses it is essential that adequate microbiological data have been 63 accumulated. In particular, data should be generated to describe the range of MICs of the test agent 64 against individual species, genera or organism groups (e.g. enterobacteria) relevant to the proposed 65 indications. Additionally, time-kill studies should be conducted to provide preliminary insight into the 66 relationship between test agent concentration and antimicrobial activity. 67 PK-PD indices may be identified from in-vitro and/or in-vivo PD models, leading to establishment of 68 nonclinical PD targets (PDTs) for the most important pathogens relevant to the intended clinical uses. 69 The determination of the probability of target attainment (PTA) using simulations to support dose 70 regimen selection requires adequate clinical PK data and the use of population PK (POPPK) models. 71 Initially these PK data will come from healthy volunteers. Since there may be important differences in 72 PK of the test agent between healthy volunteers and patients with acute infections the simulations 73 used for preliminary assessments of PTA may need to be adjusted to anticipate the possible effects of 74 infection-related systemic disturbances on PK. The PTA should be re-assessed when PK data have been 75 obtained from patients with ongoing infectious processes. Other factors to take into account in 76 simulations include test agent concentrations in body fluids (such as lung epithelial lining fluid) and the 77 effects of other interventions such as positive pressure ventilation. 78 The evaluation of clinical exposure-response (E-R) relationships and their use to derive clinical PDTs is 79 an evolving field. There are several reasons why clear conclusions may not always be reached. 80 Nevertheless, it is recommended that sponsors plan to obtain sufficient PK data from patients enrolled 81 in studies of clinical efficacy to support these analyses. 82 The identification of beta-lactamase inhibitor dose regimens has also emerged as an important area for 83 use of PK-PD analyses. As for antimicrobial agents the PK-PD index and PDT should be identified for 84 each inhibitor and simulations should be conducted that take into account the variability in PK of the 85 inhibitor and the partner beta-lactam agent. 86 The use of PK-PD analyses to identify potentially efficacious dose regimens has reduced or, in some 87 cases, replaced the need for clinical dose-finding studies during the clinical development of new 88 antimicrobial agents, allowing more rapid progress to pivotal efficacy studies. For reasons of lack of 89 feasibility and/or as part of abbreviated clinical development programmes of test agents with a 90 potential to address an unmet need there may be very limited clinical efficacy data generated to 91 support application dossiers. In these cases it is essential that there are very robust PK-PD analyses to 92 support the likely adequacy of the dose regimen and any dose adjustments that may be needed for 93 special populations. Since the PK-PD index and PDTs do not change in the presence of bacterial Guideline on the use of pharmacokinetics and pharmacodynamics in the development of antibacterial medicinal products EMA/CHMP/594085/2015 Page 3/21 94 mechanisms of resistance that may have some impact on the MIC of the test agent, the PTA can be 95 used to predict whether or not a test agent is likely to have useful clinical activity against specific 96 multidrug-resistant organisms. This is especially important when such organisms are rare so that very 97 few are likely to have been treated in pre-licensure clinical studies and it may be very difficult to 98 interpret the clinical outcome data. 99 1. Introduction(background) 100 The Points to consider on pharmacokinetics and pharmacodynamics in the development of antibacterial 101 medicinal products (CPMP/EWP/2655/99) was developed at a time when the use of pharmacokinetic 102 (PK) and pharmacodynamic (PD) indices to select potentially effective dose regimens for antibacterial 103 agents was gaining importance. In the years elapsed since the adoption of CPMP/EWP/2655/99 there 104 have been considerable advances in the field of pharmacometrics. Meanwhile regulatory experience 105 has been gained from provision of scientific advice and from review of application dossiers in which 106 dose regimens have been based primarily on identification of PK-PD indices and targets (PDTs) and the 107 application of modelling and simulation to determine the probability of target attainment (PTA). 108 Since CPMP/EWP/2655/99 was issued the role of PK-PD analyses in dose regimen selection has gained 109 increasing importance. For example, in-vitro PD models and PK-PD analyses have minimised or 110 replaced clinical dose-finding studies and have emerged to be of great assistance in identifying dose 111 regimens for beta-lactamase inhibitors. In the case of antibacterial agents that can address an unmet 112 need, PK-PD analyses play a central role in dose regimen selection. Moreover, increasing reliance has 113 been placed on the use of PK-PD analyses to select dose regimens for special populations (including 114 children and those with renal impairment) and to assess the potential clinical importance of the effects 115 of intrinsic and extrinsic factors on PK. 116 Other developments include the use of PK-PD analyses to select regimens that may minimise the risk 117 of selecting for resistant organisms, which is gaining acceptance as experience grows in this field. 118 Furthermore, several application dossiers have demonstrated how analyses of exposure-response (E- 119 R) relationships can provide further support for dose regimens and dose adjustments in specific patient 120 populations as well as having other potential uses. 121 When developing new antimicrobial agents and when planning to add or modify dose regimens for 122 approved agents sponsors either have and/or obtain external expertise when performing analyses of 123 PK-PD relationships. Nevertheless, there are some crucial aspects of the data, analyses and 124 interpretation of the findings that deserve attention in a regulatory guidance document. This Guideline 125 has been developed to outline the regulatory expectations for application dossiers and reflects both the 126 scientific advances and the regulatory experience. In a field that is continually advancing the Guideline 127 does not attempt to provide detailed guidance on issues such as methodologies for modelling and 128 simulation. 129 2. Scope 130 This Guideline is intended to be applicable to antibacterial agents, including antimycobacterial agents, 131 as well as antifungal agents. The focus is on the use of PK-PD analyses to identify potentially 132 efficacious dose regimens. The conduct of PK-PD analyses to explore the relationship between PK of 133 the test antimicrobial agent and selected safety parameters is not addressed. 134 The Guideline addresses the following: Guideline on the use of pharmacokinetics and pharmacodynamics in the development of antibacterial medicinal products EMA/CHMP/594085/2015 Page 4/21 135 a. The microbiological data that should be accumulated to support PK-PD analyses, including 136 descriptions of MIC distributions and the conduct of time-kill studies to obtain preliminary 137 information on the relationship between drug concentrations and antimicrobial effects. 138 b. The identification of PK-PD indices and PK-PD targets (PDTs) from nonclinical data, 139 including the use of in-vitro and/or in-vivo PD models. 140 c. The clinical PK data needed to support PK-PD analyses at various stages of the clinical 141 development programme. 142 d. The determination of the probability of target attainment (PTA) using simulations to support 143 dose regimen selection. 144 e. The evaluation of clinical exposure-response (E-R) relationships using data that are 145 collected during clinical studies that assess clinical and microbiological outcomes in patients. 146 f. Identification of beta-lactamase inhibitor dose regimens. 147 g. The extent to which the results of PK-PD analyses may support or replace clinical data. 148 The same PK-PD analyses used to identify and confirm potentially efficacious dose regimens are at the 149 cornerstone of setting interpretive criteria for susceptibility testing. This Guideline does not specifically 150 address the use of pharmacometrics to identify susceptibility testing interpretive criteria. Nevertheless, 151 the Guideline takes into account the data requirements and PK-PD analyses recommended by EUCAST 152 [19] and the CLSI [6] for the purpose of setting interpretive criteria. 153 3. LEGAL BASIS 154 This Guideline has to be read in conjunction with the introduction and general principles of the Annex I 155 to Directive 2001/83 as amended as well as other pertinent EU and ICG guidelines and regulations, 156 especially the following: 157 Guidance on evaluation of medicinal products indicated for the treatment of bacterial infections 158 (CPMP/EWP/558/95 Rev 2) 159 Addendum to the guideline on the evaluation of medicinal products indicated for treatment of bacterial 160 infections (EMA/CHMP/351889/2013) 161 Guideline on the Investigation of Drug Interactions (CPMP/EWP/560/95/Rev. 1) 162 Dose-Response Information to Support Drug Registration – CPMP/ICH/378/95 (ICH E4) 163 Clinical Investigation of Medicinal Products in the Paediatric Population - CPMP/ICH/2711/99 (ICH E11) 164 Note for Guidance on population exposure: The Extent of Population Exposure to Assess Clinical Safety 165 for Drugs - CPMP/ICH/375/95 (ICH E1A) 166 Guideline on the role of pharmacokinetics in the development of medicinal products in the paediatric 167 population (EMEA/CHMP/EWP/147013/2004) 168 Guideline on the evaluation of the pharmacokinetics of medicinal products in patients with impaired 169 hepatic function (CPMP/EWP/2339/02) 170 Note for guidance on the evaluation of the pharmacokinetics of medicinal products in patients with 171 impaired renal function (CHMP/EWP/225/02) Guideline on the use of pharmacokinetics and pharmacodynamics in the development of antibacterial medicinal products EMA/CHMP/594085/2015 Page 5/21 172 Guideline on reporting the results of population pharmacokinetic analyses 173 (EMEA/CHMP/EWP/185990/2006) 174 Note for Guidance on General Considerations for Clinical Trials (ICH E8, CPMP/ICH/291/95) 175 Note for Guidance on Guideline for Good Clinical Practice (ICH E6, CPMP/ICH/135/95) 176 4. Main guideline text 177 4.1. Microbiological data 178 Section 4.1.1 of the Guideline on the evaluation of medicinal products indicated for treatment of 179 bacterial infections (CPMP/EWP/558/95 Rev 2) outlines the microbiological data that should be 180 collected to support an application dossier for a new antibacterial agent. The guidance provided is also 181 applicable to the accumulation of sufficient in-vitro microbiological data to underpin the identification of 182 potentially efficacious dose regimens. In particular: 183 • To describe the spectrum of activity of the test agent 184 • To identify from the spectrum the types of infections that may be treatable and 185 • To describe the MIC distributions for the most important pathogens relevant to the indications 186 likely to be pursued 187 The guidance is also applicable when sponsors wish to add new indications involving different 188 pathogens that may require alternative dose regimens compared to the approved indications. 189 In addition, for the purposes of supporting PK-PD analyses the following investigations may be of 190 particular importance whenever appropriate to the test agent and the intended clinical uses: 191 • A description of MIC distributions based on clinical isolates obtained from patients with types of 192 infections that fall within the intended range of indications for the test agent 193 • Time-kill studies 194 • Assessment of any post-antibiotic effect [7] 195 • Evaluation of intracellular antimicrobial activity 196 • Evaluation of MICs of the test agent in the presence of a range of resistance mechanisms and, 197 applicable, against any target species that demonstrate hetero-resistance 198 • Identification of organism subtypes (e.g. genotypes or serotypes) that have higher or lower 199 rates of resistance, which may be mechanism-specific, compared to other subtypes 200 The MIC data generated should be presented for entire populations by species, genus or organism 201 grouping (e.g. enterobacteria or beta-haemolytic streptococci of groups A, B, C and G) and also 202 separately for subsets with and without acquired resistance. The latter display may not be applicable 203 for a new agent of a new class against which pre-existing resistance is not detected amongst large 204 collections of recent clinical isolates. 205 The data should suffice to identify appropriate MICs of the test agent to be used in analyses to 206 describe the PTA (see section 4.4). That is, having derived PK-PD indices and PDTs as described in 207 section 4.2, to assess the PTA at selected MICs of the test agent when using specific dose regimens to 208 treat relevant pathogens. Consideration should be given to selecting the MICs from the distributions Guideline on the use of pharmacokinetics and pharmacodynamics in the development of antibacterial medicinal products EMA/CHMP/594085/2015 Page 6/21 209 for isolates obtained from patients with the types of infection targeted by the test agent. If the test 210 antimicrobial agent is proposed to have useful activity against organisms that are resistant to other 211 agents in the same class the typical MICs of the test agent for this subgroup should be at or below the 212 highest MIC at which PTA is assessed. 213 The MIC values used for PTA analyses usually encompass values across the range observed. The MICs 214 should always include values at the upper end of the MIC distribution and would usually include the 215 MIC for each pathogen or group of pathogens of interest and/or the epidemiological cut-off values 90 216 (ECVs) for each species of interest. If ECVs are used they should be derived from an adequate 217 collection of isolates within a single species and the methodology used to ECV identification should be 218 described (e.g. simple visualisation of histograms or a mathematical approach [31]). 219 Section 4.1.2 of CPMP/EWP/558/95 Rev 2 addresses the microbiological data that should be collected 220 during clinical efficacy studies. These data can be used to further substantiate the MIC distribution 221 curves for individual organisms and are necessary for the evaluation of clinical exposure-response (E- 222 R) analyses in which relationships between documented or predicted PK parameters and MICs of the 223 agent against pathogens in individual patients are explored (see section 4.5). 224 4.2. Determining PK-PD indices and PK-PD targets 225 4.2.1. Introduction 226 The pharmacokinetic-pharmacodynamic index (PK-PD index) represents the quantitative relationship 227 between a pharmacokinetic measure of exposure to the test agent (such as AUC) and a microbiologic 228 measure of bacterial susceptibility (such as MIC) [8, 20]. 229 It has been shown that PK-PD index values derived from studies in animals and those obtained by 230 Classification and Regression Tree (CART) analysis of clinical exposure-response data are very similar 231 [19]. Potentially a PK-PD target (PDT; a magnitude for a PK-PD index at which a desired level of 232 predicted response is achieved) can be derived from nonclinical and/or clinical studies. 233 During development programmes for new antimicrobial agents the PDT is derived (at least initially) 234 from nonclinical rather than clinical studies (see also section 4.5). These may include nonclinical in- 235 vivo studies in animal models and/or in-vitro PD models (e.g. chemostat and hollow fibre infection 236 models). Before proceeding to conduct studies using these models the microbiological data described 237 in section 4.1, including time-kill studies, commonly provide initial insight into PK-PD index or indices 238 most likely to be associated with efficacy. For example: 239 • When a concentration-dependent pattern of bactericidal activity is observed in time-kill studies, 240 the AUC :MIC ratio and/or the C :MIC ratio is/are usually found to be predictive of efficacy 0-24 max 241 in PK-PD model systems. 242 • When a time-dependent pattern of bactericidal activity is observed in time-kill studies the 243 %Time>MIC and/or the AUC :MIC ratio is/are usually found to be predictive of efficacy in 0-24 244 PK-PD model systems. 245 For most antimicrobial agents it should be possible to identify specific nonclinical PK-PD index values 246 (PDTs) for each pathogen or group of pathogens of interest that result in: 247 - A net static effect, i.e. no log drop in colony forming units (CFU) 10 248 - A 1 log drop in CFU 10 Guideline on the use of pharmacokinetics and pharmacodynamics in the development of antibacterial medicinal products EMA/CHMP/594085/2015 Page 7/21 249 - A 2 log drop in CFU 10 250 4.2.2. Nonclinical PK-PD studies 251 The PK-PD index or indices most closely related with efficacy of an antimicrobial agent should be 252 identified from nonclinical PK-PD infection models, which may be conducted in vitro and/or in 253 appropriate animal models. In general, the use of in-vitro models is recommended initially so that i) 254 there is no restriction on the number of organisms that can be tested ii) any studies that are 255 conducted in animal models can be kept to a minimum iii) animal models can be used to answer 256 specific questions that are not adequately addressed by in-vitro models. 257 The organisms used in the models should be representative of those most relevant to the intended 258 clinical uses and should exhibit MICs of the test agent that include values at the upper end of the wild- 259 type distribution (see section 4.1). It is recommended that a core set of organisms should be used in 260 all the nonclinical models, to which others may be added in specific models. Generally it is suggested 261 that ~4-5 organisms of the major target species or organism groups should be tested but fewer may 262 be tested in in-vivo models and others tested in in-vitro models. 263 Evidence to date indicates that the PK-PD index for a specific antimicrobial agent and pathogen is not 264 affected by the presence of mechanisms of resistance that result in MICs of the test agent far above 265 the upper end of the wild-type distribution. In addition, the PK-PD index should not be affected by 266 mechanisms that confer resistance to other agents in the same class or those that have a similar 267 spectrum of activity as the test agent, with or without any impact on MICs of the test agent. 268 Nevertheless, sponsors may wish to select the organisms to be tested in the models on the basis of 269 specific phenotypes in order to confirm these expectations. 270 If the test agent is of a known class it may be useful to include an active comparator from the same 271 class as an internal control at least in in-vitro models. 272 In-vitro models 273 In-vitro models have several advantages over animal models. In particular, in-vitro models make it 274 possible to: 275 • Derive PK-PD indices based on larger numbers of representative organisms and a wider range 276 of inocula than is possible and justifiable in animal models 277 • Assess the effects of multiple different PK profiles. Initial studies can be conducted before there 278 are any clinical PK data available to derive nonclinical PK-PD indices and PDTs. Once clinical PK 279 data have been generated the models can be used to simulate typical plasma/serum profiles 280 expected in infected patients (which may be based on POPPK model predictions of typical PK 281 profiles) and assess the effect on organism numbers to provide further support for the PDTs 282 [15]. 283 • Study the relationships between rates of emergent resistance, drug exposure and duration of 284 therapy [10, 11, 17] 285 • Compare the test agent with other agents of the same class or, at least, other agents with a 286 similar spectrum of activity 287 The most widely used in-vitro models have been the chemostat and hollow fibre models. Other models 288 may be acceptable subject to provision of adequate data to describe assay performance and 289 sensitivity. Guideline on the use of pharmacokinetics and pharmacodynamics in the development of antibacterial medicinal products EMA/CHMP/594085/2015 Page 8/21 290 Animal models 291 Most animal models involve mice. In the commonly used neutropenic mouse thigh and lung infection 292 models [1, 9] mice are rendered neutropenic and then infected with an estimated inoculum of colony 293 forming units (CFU; confirmed retrospectively from plating the inoculum and determination of colony 294 counts) in the thigh or lung that is known to be sufficient for assay sensitivity (i.e. to be able to detect 295 differences between untreated control groups and groups given the test agent if such a difference 296 exists). Treatment is initiated and blood sampling for determination of test agent concentrations (or 297 test agents if combinations are under evaluation) is conducted at appropriate intervals based on prior 298 PK studies and total bacterial counts are determined for designated tissues/organs at pre-determined 299 time points. Plasma/serum exposures using different doses and/or dose intervals are plotted against 300 CFU. 301 Other nonclinical models (e.g. using non-neutropenic mice or using other species) may be used if 302 supported by adequate data, such as a demonstration of the correlation of the results with neutropenic 303 mice. Additional specialised models may be used if the test agent is proposed to treat infections at 304 sites where plasma/serum levels may not be predictive of compartmental levels, such as in meningitis 305 and in infections involving intracellular organisms (such as M. tuberculosis and L. monocytogenes). 306 4.2.3. Analyses of PK-PD relationships 307 Sponsors should provide details of the analysis methods used with the model parameters and 308 goodness of fit. For example, in the common case that a Hill-type function is fit to PK-PD data the 309 report should include the E , EC , E and Hill’s constant. 0 50 max 310 PK-PD indices should be expressed as a function of free drug concentrations or there must be a 311 justification why total drug is used. 312 As a minimum the analyses should report the magnitude of the PK-PD indices (i.e. PDTs) necessary to 313 achieve net bacterial stasis, 1- and 2-log reductions in bacterial densities for each pathogen or group 10 314 of pathogens of interest, taking into account that not all agents will achieve 2-log reductions or, at 10 315 least, not for all pathogens. Section 4.4.2 considers factors to be taken into account when selecting 316 PDTs for use in analyses of PTA. 317 Sponsors may propose an extrapolation of PDTs that are based on actual data with specific organisms 318 to other organisms that commonly behave similarly, i.e. have been shown to have the same PK-PD 319 indices and similar PDTs for antimicrobial agents closely related to the test agent. 320 4.3. Clinical pharmacokinetic data to support PK-PD analyses 321 Human PK data are critical for selection of potentially effective dose regimens. Population PK (POPPK) 322 models should be developed in accordance with CHMP guidance in order to predict human exposures to 323 the test agent (see section 4.4) and for analyses exploring exposure-response (E-R) relationships in 324 the target patient population (see section 4.5). 325 4.3.1. PK data from uninfected subjects 326 The initial PK data will come from healthy volunteers in whom intensive PK sampling is possible after 327 single and multiple doses. These data should be sufficient to describe the PK properties of the test 328 antimicrobial agent, including plasma/serum profiles and routes of metabolism and elimination. As Guideline on the use of pharmacokinetics and pharmacodynamics in the development of antibacterial medicinal products EMA/CHMP/594085/2015 Page 9/21 329 appropriate, the effects of renal and/or hepatic impairment may need to be assessed. An initial POPPK 330 model may be based solely on data from healthy subjects and can be used for the preliminary 331 assessment of potentially efficacious doses for use in patients. 332 333 4.3.2. PK data from infected patients 334 The PK profile of a test antimicrobial agent in the infected target patient population may demonstrate 335 several important differences compared to healthy volunteers. For example, some oncology patients 336 and some intensive care unit patients, with or without ongoing infections, have been found to be in a 337 state of renal hyperfiltration, whereby doses or dose frequencies of renally excreted agents may need 338 to be adjusted to achieve the desired PTA. Another example is that active infection may alter the 339 volume of distribution of the test agent and so impact on plasma/serum levels. On occasion the 340 mean/median values for PK parameters may be similar between healthy volunteers and patients but 341 inter-individual variability is considerably greater in the patients even in the absence of significant 342 organ dysfunction and/or changes in plasma proteins. In addition, covariates that have a significant 343 effect on PK in infected patients may not impact on PK in healthy volunteers. 344 In initial studies with a test antimicrobial agent in infected patients or when an established agent is to 345 be used in a new indication it is recommended that intensive PK data are obtained from a subset and 346 sparse sampling PK data are obtained from the total study population assigned to the test agent. The 347 PK data obtained from patients typical of the intended target population in terms of site of infection 348 and severity of infection (but regardless of pathogen susceptibility) should be used to update the 349 POPPK model. The updated model can then support repeat PK-PD analyses to confirm or reject the 350 likely sufficiency of the dose regimen before proceeding to larger studies in patients. 351 In order to support analyses of clinical E-R relationships (see section 4.5) it is recommended that 352 sponsors plan for sparse sampling of all patients in pivotal clinical efficacy studies. 353 4.3.3. Other relevant data 354 The degree of binding of the test agent to human plasma proteins in the presence of clinically relevant 355 concentrations should be assessed. Initially this may be evaluated in vitro by spiking human plasma 356 with different concentrations of the test agent to determine whether there is concentration-dependent 357 binding. Further estimates should be obtained during a study with radiolabelled test agent (if 358 conducted) or from samples collected during clinical PK studies. The data collected from infected 359 patients should suffice support a robust estimation of unbound (free) concentrations of the test agent 360 that can be used for PK-PD analyses. 361 As relevant to the test agent and its intended clinical uses, total and free test agent concentration-time data 362 should be presented for specific body fluids and compared related to plasma/serum levels using 363 compartmental PK modelling. At the present time it is considered important to provide data on the 364 following: 365 • Urinary concentrations when a significant amount of the test agent is excreted unchanged in 366 urine and it is intended for treatment of urinary tract infections. 367 • Epithelial lining fluid (ELF) free drug concentrations when the test agent is to be used to treat 368 pneumonia. Typically, these studies are conducted in uninfected patients each of whom is Guideline on the use of pharmacokinetics and pharmacodynamics in the development of antibacterial medicinal products EMA/CHMP/594085/2015 Page 10/21