Frontiers in Anti-Infective Drug Discovery: Volume 9 E-Book
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- Herausgeber: Bentham Science Publishers
- Kategorie: Fachliteratur
- Serie: Frontiers in Anti-Infective Drug Discovery
- Sprache: Englisch
This book series brings updated reviews to readers interested in advances in the development of anti-infective drug design and discovery. The scope of the book series covers a range of topics including rational drug design and drug discovery, medicinal chemistry, in-silico drug design, combinatorial chemistry, high-throughput screening, drug targets, recent important patents, and structure-activity relationships.
Frontiers in Anti-Infective Drug Discovery is a valuable resource for pharmaceutical scientists and post-graduate students seeking updated and critically important information for developing clinical trials and devising research plans in this field.
The ninth volume of this series features 5 reviews that cover some aspects of clinical and pre-clinical antimicrobial drug development, with 2 chapters focusing on drugs to treat leishmaniasis and dengue fever, respectively.
- Use of preclinical and early clinical data for accelerating antimicrobial drug development
- Post-translational modifications: host defence mechanism, pathogenic weapon, and emerged target of anti-infective drugs
- Scope and limitations on the potent antimicrobial activities of hydrazone derivatives
- Current scenario of anti-leishmanial drugs and treatment
- Dengue hemorrhagic fever: the potential repurposing drugs
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Veröffentlichungsjahr: 2021
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PREFACE
Infections caused by viruses, bacteria, fungi, and parasites have caused death and suffering to humans and other life forms since their existence. Discoveries of antibiotics in 1928 and the development of vaccines were two important landmarks in our fight against harm caused by these unseen enemies. However, various infection-causing agents soon developed resistance against almost all available antibiotics, making the treatment an ever-growing challenge. Similarly, many fast mutating viruses have rendered many vaccines ineffective. The COVID-19 pandemic has posed an extensional threat to the human race, and it is a stark reminder of our vulnerabilities against infections. To meet these re-emerging challenges, it is imperative to constantly understand the molecular basis of infections and drug resistance, identify new drug targets and develop new chemotherapeutic agents. Unfortunately, till recently, pharmaceutical research for anti-infective agents has been given a low priority due to “economic feasibility” considerations. Only in the last decade, the topic has received global attention and vigorous research pursued in academic and pharmaceutical laboratories.
The book series “Frontiers in Anti-infective Drug Discovery” has been publishing review articles on key aspects of this field. Volume 9 is not different from the previous well-received volumes. It contains 5 carefully selected reviews on various key stages of drug development and approaches against infections caused by bacteria and parasites. The Review by Samatani et al is focused on a critically important aspect of drug development i.e. optimal choice of dose regimen. They explain the use of in vitro data against pathogens, animal PK/PD, clinical pharmacokinetics, and Monte Carlo simulations in this process. The chapter by Fadauloglou et al is focused on the role of post-translational modifications (PTMs) of microbial and host proteins during a successful bacterial and viral invasion in host cells. As a result, PTMs have recently emerged as novel and promising targets for the discovery of new anti-infective therapies.
Jean Michael Brunel has contributed an article on the potential of hydrazine-based agents as novel drug candidates against resistant bacteria and fungi, as well as their structure-activity relationships. The development of drugs against the second most important neglected parasitic disease, leishmaniasis, is the focus of the chapter by Roy and Mazire. They have reviewed the literature on various new therapeutic options and their current stages of development against this debilitating poor man's disease. Dengue viral hemorrhagic fever is also an important health challenge for the developing world. Leowattana et al. have commented on various re-purposed drugs currently in various stages of development against dengue virus infection and its various forms.
The 9th volume of this important book series comprises scholarly contributions from several leading experts to whom we are indebted. “Team Bentham” also deserves our appreciation for a job very well done. Among them, Ms. Asma Ahmed (Senior Manager Publications), and Mr. Mahmood Alam (Editorial Director) of Bentham Science Publishers have played a key role in the timely completion of the volume in hand. We sincerely hope that the efforts of authors and the production team will help readers to better understand and appreciate the importance of vigorous research and development activities currently underway against infections that cause tremendous suffering to humanity.
List of Contributors
Use of Preclinical and Early Clinical Data for Accelerating Antimicrobial Drug Development
Mahesh N. Samtani1,*,Amarnath Sharma1,Partha Nandy1Abstract
Antimicrobial drug development over the last two decades suggests that the choice of dose and dosing regimen can be selected at a very early stage. This is achieved by optimizing several key factors that are properties of the drug, the bug, and the host species. Drug exposure metrics, relative to the potency of the drug, are computed during the early stages of anti-infective drug development. These metrics serve as predictors of efficacy in the animal models of infection. Drug exposure relative to its potency can be expressed using a few metrics such as AUC/MIC, T>MIC, or Cmax/MIC. The class of drugs that the anti-infective belongs to often determines the optimal choice of the metric for a given anti-microbial (and is empirically chosen based on pre-clinical data). There are various anti-microbial drug classes available on the market. Despite a large number of drug classes, there is reasonable consensus that the PK/PD target, i.e. metric of relative drug exposure described above, obtained from in vitro and animal experiments can predict the efficacy of specific drugs in humans. The steps involved in the derivation of this crucial PK/PD metric and dosing regimen in humans are as follows: (a) First, the metric is chosen and then the magnitude of the metric is computed using in vitro and animal PK/PD experiments; (b) Next, drug properties such as plasma protein binding are included as correction factors for the PK/PD target; (c) Finally, the non-clinical information is combined with early clinical pharmacokinetic data to estimate which dosing regimen has the greatest probability of attaining the PK/PD metric. This methodology of computing the dosing regimen and estimating the probability of successful target attainment accounts for two key sources of variability. These are between-patient variation in clinical pharmacokinetics and the gamut of MIC values that reflect the susceptibility of pathogens to the anti-microbial drug. These sources of variability are incorporated by running Monte Carlo simulations that are population-based in nature i.e. they account for variability in both the pathogen and the host. These sophisticated simulations answer the critical question around the rate of target attainment for dosing regimens of the new antibiotic drug. In summary, combining in-vitro data, animal PK/PD, early clinical pharmacokinetics, and Monte Carlo simula-tions expedites decision making in antimicrobial drug development. These efficiencies
can lead to earlier and faster entry into full development for anti-microbials and aid optimal choice of dose regimen for phase 2/3 studies.
INTRODUCTION
Drug development of antimicrobials over the last 2 decades has been revolutionized by the pragmatic selection of dose and dosing regimens driven by limited but well defined and validated factors that are characteristics of the drug, the pathogen and the host [1]. A robust predictor of anti-microbial efficacy is achieving the pharmacokinetic/pharmacodynamic (PK/PD) target i.e. a drug exposure metric such as area under the curve (AUC) or % time above minimum inhibitory concentration (%T>MIC) or peak concentration (Cmax) relative to the susceptibility of the organism. Despite a large number of classes of antimicrobial agents, there is increasing consensus that PK/PD targets from in-vitro and in vivo. preclinical studies are predictive of efficacy in humans [1].
One way of utilizing the PK/PD target is to examine whether the free plasma drug concentrations required for anti-microbial efficacy based on preclinical data, can be safely achieved in early human trials. The technique of examining the adequacy of different regimens to treat a myriad of pathogens is based on Monte Carlo simulation methods that allow assessment of how frequently specific doses of the new drug are expected to achieve therapeutic targets. This methodology has the potential to help with study design for subsequent phases of drug development whereby only those doses with a high probability of success are selected. The antimicrobial development process starts off with assessing antimicrobial activity of an agent in vitro against several different laboratory strains of microbes, followed by in vivo studies in appropriate animal models with microbes of interest where the right PK/PD target is established. The pathogens causing the infection stay the same across species and this allows translation of efficacy from animals to humans. The PK/PD target is also species independent because the pathogen is susceptible in any species as long as the PK exposure is achieved. The PK/PD target is both a drug and bug property since it allows tailoring the exposure relative to the pathogen’s susceptibility e.g. exposure should increase with decreasing susceptibility [2]. This is followed by assessing pharmacokinetic characteristics of the drug in healthy human volunteers. Utilizing the totality of such information and reinforcing the knowledge surrounding susceptibility and prevalence of antibacterial strains of interest in the community, extensive Monte-Carlo simulations are undertaken to ascertain the right dose and dosing regimen for a given indication. The objectives of the Monte-Carlo analysis are to (i) describe the population pharmacokinetic (PK) behavior of a novel anti-microbial in development by capturing the absorption and disposition properties using plasma concentrations collected during Phase 1 studies; (ii) to assess the expected performance of various doses and dosing regimens in clinically attaining PK/PD target measures associated with in-vivo efficacy in animal models over a range of pathogen susceptibilities using Monte Carlo simulations; and (iii) utilize the results of the Monte Carlo simulations to identify the optimal dose and dosing regimen for subsequent stages of drug development. The magnitude of the PK/PD target is generally obtained from the murine thigh infection model (but the animal model can vary depending on the infection being treated) and correction factors such as plasma protein binding are incorporated to adjust for species differences. Human PK data are usually obtained from early Phase 1 clinical studies. The pre-clinical efficacy information is then combined with the human PK data to determine which clinical dose has the highest probability of achieving the desired PK/PD target. These dosing computations and the calculation of the probability of successful target attainment explicitly account for inter-subject variability in human PK parameters during simulations, the relative natural prevalence of pathogens for target attainment, and the variability in pathogen susceptibility to allow dual individualization of pathogen and humans to the drug. The results from such exercise aids decision making for the development of novel antimicrobials. These decisions encompass the transition of a novel drug entity into full clinical development and the selection of dosing regimens for future phase II/III trials or making a “no-go” decision if PK/PD target attainment is lower than 90%.
Drug, Bug and Host Interactions: Five Critical Factors
Infections caused by multidrug-resistant bacteria are a serious threat to the general population and continue to cause significant morbidity and mortality worldwide. Application of bio-simulations that allow integration of prior information about the variability in human PK and pathogen susceptibility for assessing the likelihood of success for clinically chosen dose and dosing regimens has increased tremendously in the last 2 decades. The utility of Monte Carlo simulations for dose optimization of anti-microbials was first illustrated in 1998 to the FDA anti-infective drug products advisory committee for the antibiotic evernimicin [3]. Monte Carlo simulation allows integration of the knowledge about the PK profile of the drug and the differences in pathogen susceptibility to the drug to evaluate the expected likelihood of success of a given treatment in a particular disease during future clinical trials.
These bio-simulations are driven by five critical factors that describe the interaction between the drug, pathogen, and host [1]. These five factors include (i) the PK/PD target; (ii) distribution of pathogen susceptibility to the drug; (iii) variability in human PK; (iv) the drug’s protein binding characteristics; and (v) the natural frequency of pathogen occurrence within a given infection type (Fig. 1). The PK/PD target is a drug exposure metric normalized to the suscepti-bility of the organism and it serves as a predictor of drug efficacy [1]. The degree of pathogen susceptibility is obtained from in-vitro experiments that measure the minimum inhibitory concentration (MIC) required to suppress bacterial growth. Thus, drug exposure normalized by pathogen susceptibility is represented by PK/PD target metrics such as the area under the curve over MIC (AUC/MIC), peak concentration over MIC (Cmax/MIC), or percent time during a dosing interval when plasma drug concentrations are above the MIC (%T>MIC).
Fig. (1)) Sources of information for a model-based estimation of target attainment using Monte Carlo Simulations. The sources of information are represented as interconnected pieces in the outside hexagons which are necessary for computing target attainment.The choice of the three main PK/PD targets used in this analysis (Cmax/MIC, AUC/MIC, and %T>MIC during a dosing interval) varies by drug class and depends on whether the drug of interest has time-dependent or concentration-dependent killing. For concentration-dependent drugs, the antimicrobial activity depends on peak drug concentrations. Either Cmax or AUC drives the PD for these anti-infective agents, and this property is often associated with drug classes such as aminoglycosides and fluoroquinolones. They exhibit bacterial growth suppression even after limited exposure to the drug. These drugs can therefore be administered using a dosing interval that is somewhat longer than what is predicted by the PK half-life. This attribute offers fewer doses per day or per treatment and may improve adherence to antibiotics [4]. In contrast, for time-dependent killing, optimal drug effects are obtained as long as concentrations are maintained above the MIC during each dosing interval. Moreover, since sustained concentrations are required during an entire dosing interval, these drugs are often dosed intravenously as infusions and require repetitive dosing. The repetitive or continuous dosing is often not an issue for adherence since these drugs are used in critically ill patients suffering from life-threatening infections in intensive care units. Antibiotics that belong to this class include beta-lactams, carbapenems, cephalosporins etc.
PK/PD targets, as indicated above, are drug-class specific and are assumed to be similar across species because they reflect the drug’s mechanism of action responsible for the in-vivo interaction between the drug and the pathogen [1]. Therefore, during preclinical development, the PK/PD target is usually obtained from dose-fractionation experiments in the murine infection models [1]. Pathogens reside in the interstitial space between cells, and the fraction of drug that is accessible to this effect site is the free concentration in plasma [5]. Drug PK is therefore corrected for differences in protein binding between mice and humans. It is recognized that in the clinical setting, there exists between-subject variability in human PK and there is a range of MICs for pathogen susceptibility to the drug. Variability in pathogen susceptibility and PK are accounted for in a simulation model, and each factor is described by a distribution of values. Even though the PK/PD target is fixed, the target exposure to be achieved at each MIC changes. As an example, with each doubling of MIC, the target AUC needed for successful treatment also has to double so that the established target is met. This is commonly referred to as dual individualization, which means that as the pathogens become less susceptible, greater drug exposure is needed to suppress their growth [6].
The degree of variability in drug PK that produces differences in drug exposure metrics (AUC, Cmax, etc.) across individuals is obtained through drug disposition studies in the human population. If the drug hasn’t entered the clinic yet, then the pre-clinical PK can be scaled to estimate human PK using either simple allometry or physiologically based PK modeling. These estimates are then used to create an exposure metric distribution for several thousand subjects using simulations and the between-subject variability in all PK parameters, which is generally inflated to 40% coefficient of variation (CV) to reflect higher variability that is generally observed in patient populations. Similar to the PK variability in the human population, the pathogen of interest also displays variability in its susceptibility to the drug (PD variability). The probability of being inhibited at a certain MIC is therefore obtained from a large collection of bacterial isolates (usually several 100s to 1000s isolates).
Generally, a population PK model is developed using plasma concentration-time data from single and multiple ascending dose studies from healthy subjects in early clinical development. The modeling strategy delineated in this chapter helps to assess the utility of different dose and dosing regimens. The mean parameter estimates and inter-subject variability obtained from the population PK model are utilized as inputs for a series of Monte Carlo simulations. These simulations are carried out for various dose and dosing regimens to estimate the probability of attaining different PK/PD targets. These computations are performed across target disease pathogens with wide-varying susceptibilities and a diverse frequency of natural occurrence. Finally, the appropriateness of the dose and dosing regimens is judged based on the probability of attaining projected efficacious drug exposure metrics described above. Thus, these analyses are aimed at assessing the expected performance of various dosing regimens in attaining PK/PD target measures associated with in vivo efficacy over a range of MICs using Monte Carlo simulations and providing quantitative/integrated support in identifying optimal dose and dosing regimens for subsequent stages of drug development.
METHODOLOGICAL ASPECTS
Skin infection is used only as an example for illustration purposes to explain the computation process in the sections below, and the methodology consists of 4 steps:
In vitro susceptibility testingIn vivo testing in the animal model of choice to define the choice of the PK/PD metric and PK/PD target thresholdObtaining human PK data usually from normal healthy volunteersMonte Carlo simulations and incorporation of susceptibility and prevalence information from surveillance dataThese methods take the exposure-response relationship into consideration and allow examination of what-if scenarios such as the effect of administering a dose not studied during development. Monte Carlo computations are fairly rigorous and explicitly accounts for sources of variability that can impact the possibility of successful treatment with a new drug entity, which include: (i) inter-subject PK variability; (ii) formulation (and food effect if available for early phase 1 studies) on relative bioavailability; (iii) pathogen sensitivity and drug potency reflected by the PK/PD target; (iv) variability in pathogen susceptibility to the drug; and (v) natural occurrence of pathogens relevant to the clinical scenario.
Model for In-vitro MIC Distributions and Pathogen Frequency of Natural Occurrence
Based on the target product profile and susceptibility of pathogens for a given infection to the drug, target indications are chosen. For this exercise, we will consider complicated skin and skin structure infection (CSSI) as the main target indication. CSSI and pneumonia are used as example infections throughout the text and they are used only for illustration purposes. The computations illustrated for CSSI as an example are also applicable to other infections as well. The pathogens primarily responsible for CSSI are Enterobacteriaceae spp. (13.1%), Enterococcus faecalis (4.2%), Pseudomonas aeruginosa (8.0%), Staphylococcus spp. (65.4%), and Streptococcus spp. (9.3%). The percentages in brackets rep-resent the natural frequency of occurrence of these pathogens in CSSI, and these were obtained from the literature [7]. Moreover, the in-vitro activity was represented as the entire distribution of MICs against Enterobacteriaceae spp. (n=101), Enterococcus faecalis (n=101), Pseudomonas aeruginosa (n=98), Staphylococcus spp. (n=249), and Streptococcus spp. (n=149). The MIC distribution used here is the susceptibility of CSSI pathogens to an antibiotic ceftobiprole based on frequencies reported in the literature [8]. The natural frequency of occurrence is a disease-specific parameter, while the MIC distribution is a drug-specific parameter obtained in-vitro from a collection of bacterial isolates for each new anti-bacterial agent under development.
Model for In-vivo PK/PD Target
The choice of in-vivo animal model depends on (a) the PK of the drug in the animal species of interest; (b) the type of infection that will be the target indication in the clinic; and (c) the pathogen under study. Infections are studied in their respective organ sites in the animal model e.g. lung infection models are used to evaluate pneumonia. Other such examples of specific body site animal infections include pyelonephritis, peritonitis, meningitis, osteomyelitis, and endocarditis. However, by far the most common model for studying in-vivo antimicrobial activity of new drug candidates is the murine neutropenic thigh infection model [9]. Immunocompromised neutropenic mice were infected with the pathogen of interest in the posterior thigh muscles. After bacterial inoculation, various incremental and fractionated doses of the drug are administered in different groups of mice. One day after drug administration, the mice are sacrificed and the thigh muscles are collected for quantitative cultures. PK is usually established in a separate group of neutropenic mice infected with the same pathogens used in the dose-fractionation studies.
An inhibitory sigmoid model is used to characterize the in-vivo antimicrobial activity. Various PK/PD metrics (AUC/MIC, Cmax/MIC, %T>MIC) are used as the independent variable, while the microbial load characterized by the logarithm of colony forming units per gram of tissue (log10 CFU/g) is the dependent variable. The model is used to determine (i) which PK/PD metric best captures the sigmoidal relationship; and (ii) the magnitude of the best-chosen PK/PD metric associated with bacterial stasis (when the bacteria have reached the same log10 CFU/g as inoculated). Bacteriostasis is generally sufficient because once the drug achieves static effect in humans, the immune system can clear the infection [2]. The PK/PD threshold obtained from the murine infection model is essential for guiding clinical dose selection because it is correlated with clinical outcome [1, 3] i.e. the PK/PD metric is identical across species once corrected for protein binding since this is a drug-specific property that is not dependent on the species that is infected with the bug and in which the PK is measured. The estimated PK/PD target is often derived from multiple strains of pathogens and the average is taken across the strains to get a refined PK/PD target [10]. Assuming the plasma free fraction in mouse and man are 0.75 and 0.65, respectively, the PK/PD target is corrected for protein binding difference as follows:
(1)Population PK Parameters Representing Human Variability
At the earliest stages of drug development, a representation of the population PK parameters characterizing human variability in drug absorption and disposition can be obtained from (a) allometrically scaled PK parameters and between-subject variability of ≥40% in all PK parameters, which is generally observed in patients; or (b) a population PK model that is used to describe the earliest human plasma concentrations data collected from Phase 1 single and multiple ascending dose studies in healthy subjects (between-subject variability from Phase 1 may need to be inflated as well to reflect inter-patient variability and accommodate other uncertainties).
As an illustration, we consider an oral drug whose efficacy (PD) is driven by AUC at steady-state. The drug had a CL of 0.05 L/hr/kg in humans with a between-subject variability that is log-normally distributed with a 40% CV. Similarly, body weight was assumed to have a mean of 70 kg with a log-normal distribution and 30% CV for Monte Carlo simulations. The drug formulation was assumed to have a high relative oral bioavailability of 90% with low variability, which was simulated using a beta distribution having shape parameters of 900 and 100 (this parameter can be tweaked to assess formulation effects on PK and target attainment). The R code is shown in the appendix, it also illustrates how the computations could be performed if the PD was driven by peak concentrations rather than the area under the curve.
All calculations were performed for a drug with AUC/MIC as the PK/PD target. However, to complete the illustration, a second antibiotic was considered that is dosed intravenously as a 1-hour infusion, whose PD is driven by %T>MIC. For this illustration, the appendix (1b) with the R code shows calculations that compute fractional target attainment i.e. just the Monte Carlo simulation part for the PK. Computations after fractional target attainment are identical regardless of the PK/PD metric of interest. To illustrate %T>MIC computations, the PK parameters of an antibiotic [11] that follows 2-compartment disposition with zero-order input and first-order elimination are considered. The PK parameters and covariate effects obtained using data mostly from Phase 1 and 2 studies (and limited sparse data from Phase 3) and a population PK model are reported in Table 1.
Target Attainment Computation
The population PK parameters were used as the basis for randomly generating a dataset of 5000 subjects. The AUC for each of these virtual subjects was computed from the ratio of dose over CL multiplied by the relative bioavailability for the simulated scenario for assessing dose and formulation effects. Calculated in this manner, the AUC represents the anticipated drug exposure at steady state during the 24-hour dosing interval. Residual variability was not introduced into the calculations of simulated AUC. The randomly generated AUCs that exceed the PK/PD target scaled by the MIC (Table 2) were tallied and referred to as fractional target attainment. It is recognized in these simulations that even though the PK/PD target is fixed, the target exposure is needed to be attained at each MIC changes.
For example, if the AUC/MIC target is 100 hours, then with each MIC doubling, the target AUC must also double. This underscores the fact that as bacteria become more virulent, greater drug exposure is needed to suppress their growth. The variability in pathogen susceptibility is presented as the probability of being inhibited at a certain MIC. This probability of pathogen susceptibility for every species at each MIC was calculated by computing the fraction of strains inhibited at that MIC. The probability of attaining the PK/PD target for the virtual population of patients infected by a specific pathogen species was obtained by multiplying the fractional target attainment at each MIC by the proportion of pathogen strains with that MIC. The product of fractions at each MIC was added and the probability of achieving the PK/PD target was derived and reported in tabular format. This computation addresses the question about the probability of attaining a specific target at a specific dose against a specific pathogen species causing a particular disease such as CSSI. The process was repeated for each pathogen species and the target attainment for each pathogen species was weighted by the pathogen’s frequency of natural occurrence in CSSI. The weighted products across all pathogen species were summed to compute the final target attainment rate or TAR. The results can be plotted as a final target attainment probability as a function of relevant treatment variables such as dose, formulation, etc. This procedure is described schematically in Fig. (2). Calculation of AUCs and PK/PD target attainment computations are performed using R. A representative R script for Monte Carlo simulation for one of the dosing scenarios is provided in the Appendix.
Fig. (2)) Flow chart depicting the Monte Carlo optimization of trial success.Assumptions Adopted During Modelling And Simulation
It is important to pay close attention to model assumptions because simulation outcomes can be quite sensitive to underlying assumptions about model components. The modeling and simulation strategy delineated in the sections above utilized the following assumptions:
The PK model and the formulation effect on drug absorption are often derived from the earliest single ascending dose clinical studies. The simulations are designed to predict drug exposure at steady state upon multiple dosing and therefore, PK stationarity was assumed.Stationary PK was assumed for the intravenous antibiotic (Appendix 1b) which implies that target attainment determined from single-dose simulations apply to steady state multiple dosing scenarios. This assumption was permissible because the accumulation factor for this drug was close to one.Stationary PD was also assumed wherein time-dependent phenomena such as post-antibiotic effect and resistance development were not incorporated during the simulation process.The PK models are derived from healthy volunteer data and the PK parameters are assumed to remain unaffected in the patient population. However, inter-subject variability was inflated to reflect higher variability in patients.Inter-subject variability in protein binding was not incorporated.No formal model was developed to describe dropouts and complete compliance was assumed when simulating virtual populations. This is a fair assumption because adherence to antibiotics for life-threatening infections during acute treatment is expected to be high.The in-vitro pathogen distributions obtained from a collection of clinical isolates were assumed to be representative of the general widespread microbial population occurring in the clinical situation.The natural occurrence of pathogens was obtained from the literature. The distribution of organisms observed in this one trial was assumed to be representative of the general widespread microbial population occurring in CSSI.The uncertainty and analytical error in MIC determinations was not added as a source of variation in the Monte Carlo simulations.The PK/PD threshold derived from the murine thigh infection model is based on the bacteriostatic endpoint. Dose selection based on a bacteriostatic threshold does not apply to clinical situations involving serious or severe infections where the host immune system is compromised. For these infections, a greater magnitude of drug exposure (and dose) maybe needed to eliminate the infection.These target attainment results are often applicable to pilot formulations used in early development. However, if solid dosage formulations used in later development have a lower extent of drug absorption, then the high level of target attainment achieved with formulations may not be applicable to the solid dosage forms tested in late development.CASE-STUDY RESULTS: APPLICATION OF MONTE-CARLO APPROACH
Target Indication and Patterns of In-vitro Killing
The antibacterial spectrum of the new anti-infective agent is studied in-vitro. The appropriate indication for the drug is based on the pathogen species that are susceptible to the drug. As an example, if the drug has activity against Pseudomonas aeruginosa then a potential indication could be ventilator associated pneumonia. For adding more indications, the activity of the drug would have to be tested against additional species. These bacterial species would have to be true pathogens for indications under consideration. Importantly, the clinical pathogens tested in vitro not only have to belong to the clinical indication under consideration but should also be obtained from various geographic regions to get pathogen distributions from different countries [12]. The isolates are used to obtain the variability in pathogen susceptibility to the drug as MIC distributions. These isolates are then also used for in-vivo testing in animal models to obtain the right PK/PD target for the drug under study. In vitro experiments also demonstrate patterns of kill curves where drug effects are either concentration-dependent (higher concentration leading to more efficient killing) vs. time-dependent effect (killing occurs as long as concentrations exceed MIC). This pattern helps design the right dose fractionation experiments in animal models of infection to determine the type and extent of PK/PD target (e.g. AUC/MIC) needed for efficacy.
Pathogen Susceptibility and Natural Occurrence
The natural pathogen frequency in CSSI (as an example) is reported in Fig. (3), which shows that Staphylococcus is the most prevalent species responsible for this infection. The entire MIC distributions against these five classes of pathogens are provided in Fig. (4). If new data become available and are deemed to be quite different than the prior knowledge of pathogen susceptibility and natural occurrence, the target attainment computations must be updated to obtain the most appropriate estimate of the probability of clinical success. This update process is not illustrated, but it simply requires rerunning the codes with the updated pathogen information.
Fig. (3)) Natural frequency of occurrence of pathogens in CSSI. Fig. (4)) MIC distribution of relevant clinical pathogens in CSSI for one example antibiotic.PK/PD Target Based on The Murine Thigh Infection Model
The results from the murine study are shown in Fig. (5) and AUC/MIC was the PD-linked variable for drug efficacy. The total mouse (AUCT/MIC) target related to bacterial stasis was 87 hours and after protein binding correction, the human PK/PD target was estimated to be about 100 hours.
Fig. (5)) Murine PK/PD - Solid line represents the fit to the data. 87 hours is the murine AUC/MIC target.Clinical PK
The number of subjects available for PK model development is usually small and the underlying population model is often derived from healthy subjects. PK/PD target attainment however, reflects what to expect for a patient population. Thus, as Phase 2/3 study data become available, the target attainment computations are constantly updated to obtain the most appropriate estimate of the probability of clinical success. This update process is not illustrated here, but it simply requires rerunning the codes shown here with the updated PK model.
Monte Carlo Simulation Results
Simulation of one virtual trial with 5000 subjects is illustrated in the Appendix. The computations displayed in Table 2 provide an illustration of the methodology for the estimation of microbiological target attainment against Staphylococcus spp. by the drug at the 1000 mg dose. Fig. (4) represents the magnitude of variability in the pathogen susceptibility to the drug. The estimate of fractional target attainment based on the Monte Carlo simulation shown in Fig. (6) takes into account the variability in drug absorption and disposition in the population. Integration of these two distributions (pathogen variability and fractional target attainment), is obtained in a straightforward manner by multiplying the two to obtain the product of fractions across MIC values. To take an expectation over the entire MIC distribution, a sum of the product of fractions in the final column of Table 2 was performed to obtain a point estimate of species-specific target attainment of 95%. This value of 95% is informative because it indicates that for Staphylococcus spp, which is the most prevalent pathogen in CSSI, 95% of the patients inflicted with this pathogen are likely to achieve successful target attainment at the chosen dose and regimen. It is noteworthy that this species-specific target attainment of 95% is pertinent only to 1 dosing scheme (1000 mg daily with a formulation having high relative bioavailability), one pathogen species (Staphylococcus spp.), the PK/PD target of 100 hours, and the PK parameter estimates outlined in Table 1.
Fig. (6)) Fractional target attainment at each MIC.The next important step was to evaluate the expected attainment rates across each target pathogens for CSSI to obtain target attainment for the other 4 species (see Appendix 1a). As the last step, by accounting for the frequency of natural occurrence of pathogens (Fig. 3), a final TAR of 90% was obtained for this scenario. If the target attainment was lower than 90% then the entire process could be repeated at higher dosage strength and/or using data from other potential formulations.
SUMMARY
This work summarizes state-of-the-art data analysis of information gained from in vitro experiments, in vivo. animal studies, first in human PK profiles, Monte-Carlo methods and susceptibility/prevalence data from surveillance studies that are critical for accelerating antibiotic drug development.
The target attainment approach integrates the population PK variability (Fig. 6) with the population distribution of the MICs (Fig. 4). It is used to determine the probability of target attainment given the PK properties of the drug in the human population and the susceptibility of various microorganisms to the drug. Since the intended clinical indication is defined, and more than one bacterial species is known to cause the particular type of infection, the cumulative fraction of response in the population is based on the relative frequency of occurrence of each species. Thus, the probability obtained from the exercise illustrated in Table 2 was weighted by relative frequency of occurrence from (Fig. 3). This weighted probability is the Final TAR and the entire computation is illustrated using a sample R code (Appendix 1a).
Under- or inappropriate dosing of antibiotics is dangerous and unethical because it can lead to the emergence of resistant pathogens. Understanding the relationship between antibiotic concentration and the antimicrobial effect is essential for eradicating the pathogen and preventing the emergence of resistant pathogens. The Monte-Carlo approach represents a highly sophisticated methodology in anti-infective research for dose-selection of new drug entities. This approach maximizes %T>MIC, AUC/MIC or Cmax/MIC with the targets for stasis derived from pre-clinical models and PK parameters obtained from population PK models to compute the likelihood of target attainment for dosage regimens that may not have been studied in clinical trials with patients [13-16]. Recently mechanism-based models have been derived that assess (i) antibiotic combinations (ii) antibacterial resistance (iii) effects of the immune system (iv) bacterial cultures that contain susceptible and resistant sub-populations [13, 15]. These mechanism-based models are based on in vitro PK/PD data collected using hollow-fiber infection models [13, 15] to derive the relationship between patient PK profiles and resistance development for further in-vivo evaluations.
By taking into account sources of biological variability and by recognizing the pharmacological interaction between the pathogen, host and drug, an educated decision can be made regarding a dosing regimen that is most probable of succeeding in a future clinical trial.
As such, modeling and simulation provide a powerful, objective and transparent tool to support key decision points in the drug development program. These key decisions include the transition of a new molecular entity into full development (go/no go decision), dose selection for future clinical trials in patient populations and establishing appropriate susceptibility breakpoints for antibiotics. Simulations of this nature are also useful in devising post-marketing strategies by comparing target attainment for the new drug vs. marketed competitors in the same class or antibiotics of a different class used in the same indication. Such comparisons offer very objective criteria for deciding whether the new drug entity has merit for commencement into full development and if the product will be competitive in the market landscape. Such inferences could be based upon (i) a superior microbiological profile (ii) a more convenient dosing schedule (iii) lower cost of goods for a highly potent antibiotic that is effective at lower doses. As an example, once daily dosing will offer better compliance versus-marketed comparators that may require administration twice daily in clinical practice to obtain a similar level of target attainment. This framework is thus a general model-based drug development scaffold for the rapid advancement of drugs in the antimicrobial pipeline.
The success of this approach in antibiotic development has led to similar methodology being adopted for direct anti-viral therapy as well [17, 18
