Pivotal Role of Translation in Anti‐Infective Development

Abstract

The value of model‐based translation in drug discovery and development is now effectively being recognized in many disease areas and among various stakeholders. Such quantitative approaches are expected to facilitate the selection on which compound to prioritize for successful development, predict the human efficacious dose based on preclinical data with adequate precision, guide design, and de‐risk later development stages. The importance of time‐dependencies, which are typically species‐dependent due to different turnover rates of biological processes, is, however, often neglected. For bacterial infections, the choice of dosing regimen is typically relying on preclinical pharmacokinetic (PK) and pharmacodynamic (PD) data, because the bacterial load and disease severity, and consequently the PK/PD relationship, cannot be quantified well on clinical data, given the low‐information end points used. It is time to recognize the limitations of using time‐collapsed approaches for translation (i.e., methods where targets are based on summary measures of exposure and response). Models describing the full time‐course captures important quantitative information of drug distribution, bacterial growth, antibiotic killing, and resistance development, and can account for species‐differences in the PK profiles driving the killing. Furthermore, with a model‐based approach for translation, we can take a holistic approach in development of a joint model for in vitro, in vivo, and clinical data, as well as incorporating information on the contribution of the immune system. Such advancements are anticipated to facilitate rational decision making during various stages of drug development and in the optimization of treatment regimens for different groups of patients.

Limitations of Antibiotic MIC-Based PK-PD Metrics: Looking Back to Move Forward

“…The most predictive nonclinically derived PK-PD index and the target value of that index for various magnitudes of bacterial kill (usually stasis, or 1-or 2log 10 reduction in counts of viable bacterial cells) have been used for many years. The uses include: informing of many important steps in the development of new antibiotics, especially the choice of dosage regimens to be examined in clinical studies (Friberg, 2021); determining susceptibility breakpoints (Turnidge and Paterson, 2007;Mouton et al, 2012); and, translating to the clinical setting to select a plasma exposure target for the routine care of patients (Roberts et al, 2014;Wicha et al, 2021). The use of the PK-PD indices in these ways has undoubtedly been very helpful across the range of areas where they have been applied, as they have provided a way to semi-quantitatively describe the exposure-response relationship of antibiotics.…”

“…The use of the PK-PD indices in these ways has undoubtedly been very helpful across the range of areas where they have been applied, as they have provided a way to semi-quantitatively describe the exposure-response relationship of antibiotics. However, it is increasingly recognised that there are a number of important limitations associated with the use of these traditional PK-PD indices (Seeger et al, 2021a;Friberg, 2021;Landersdorfer and Nation, 2021). Some of the limitations relate to the nature of the nonclinical experimental models and approaches that are used to establish what is concluded to be the most predictive index, while other limitations arise from the use, in each of the indices, of the MIC as a measure of the antibacterial "potency" of the antibiotic.…”

“…There are several important limitations related to the traditional PK-PD indices, a number of which occur because only one assessment time point, usually at 24 h, is used to quantify the impact of treatment on the total bacterial population (Friberg, 2021;Landersdorfer and Nation, 2021). The first limitation arises because the time-course of bacterial response to a treatment is dictated by the balance of biological processes including the natural growth and death of bacteria, the killing of bacteria mediated by the antibiotic and any regrowth of bacteria.…”

“…Although the traditional PK-PD indices have undoubtedly been helpful in facilitating preclinical development of antibiotics, their translation into clinical studies and ultimately in proposing dosage regimens for various categories of patients, the indices are associated with several substantial limitations as discussed above. The need to capture the full time-course of both the PK of the antibiotic and the PD of its antibacterial effect, without reliance on MIC, has been increasingly recognised; fortunately, moves in this direction have already commenced (Seeger et al, 2021a;Friberg, 2021;Landersdorfer and Nation, 2021;Wicha et al, 2021).…”

“…Such models are usually based upon data from in vitro static and/or dynamic time-kill studies and can incorporate information on the time-course of effect on not only the total bacterial population but also resistant (less-susceptible) subpopulations. These models can enable translation to the clinic by being interfaced with a population PK model for the relevant patient group to predict the influence of established covariates of PK exposure (e.g., renal function, body size) on bacterial killing and development of resistance for antibiotic monotherapies and combination treatments, and also suggest dosing regimens for clinical evaluation (Bergen et al, 2016;Yadav et al, 2017;Yadav et al, 2019;Friberg, 2021). Beyond translation, mechanism-based models also have enormous potential for application in optimising the care of individual patients via model-informed precision dosing (MIPD) (Friberg, 2021;Landersdorfer and Nation, 2021;Wicha et al, 2021).…”