Design of Host-Directed Therapies using Quantitative Systems
Pharmacology
Modelling
The overall outcome of Mtb disease and treatment is reliant on the
integrated results of the molecular and cellular events, and their
reflection at tissue, organ, and host level dynamics occurring at
different time scales. As such, it can be challenging to predict patient
responses to different HDT strategies. Species differences in immune
response characteristics make it more challenging to translate the
results from preclinical studies to clinical scenarios. In addition,
determination of the effects of treatments and disease progression in
specific patient populations, can be challenging, i.e., in patients with
weakened immune response and/or other conditions, patients with specific
genotype known to affect certain pharmacology. QSP modelling can address
these hurdles through quantitative integration of Mtb host-pathogen
interaction mechanisms with PK and PD aspects of HDTs, making it a
relevant tool to guide drug discovery and development of HDTs for Mtb.
Here we discuss three main components of the QSP framework to evaluate
HDTs for Mtb infection, (1) drug PK models, (2) host immune response
models, and (3) pathogen dynamic models. The considerations for
identification of drug- and system specific parameters to facilitate
scaling, and the incorporation of variability are also discussed.
Lastly, we discuss applications of the QSP models to evaluate HDTs. An
overview of the QSP framework components and applications is provided inFigure 2 and Figure 3 , respectively.
Pharmacokinetics
Pharmacokinetics describes the concentration-time profile of drugs and
is determined by absorption, distribution, metabolism. and elimination
processes, which may differ between organisms. Consideration of
concentration-effect relationships, and therefore the PK, is of
essential value for design of HDT strategies. Mathematical PK models
quantitatively characterize PK based on parameters accounting for the
underlying processes.
Physiologically based PK (PBPK) models describe the concentration
profiles in specific tissues of interest and are informed by both drug-
and system-specific parameters. PBPK models are of relevance to scale PK
between preclinical species and towards humans in a mechanistic-fashion.
For Mtb infection, PBPK models describing lung exposure are of specific
relevance. In addition, their mechanism-based approach allows for
incorporation of drug-drug interactions, which often occur for Mtb
combination therapies[92]. In the clinical phase, quantifying
inter-patient variability in PK is important. Here, population PK
(PopPK) models are of relevance, which capture inter-individual
variation in underlying PK parameters that can be explained by specific
patient-specific covariates[93]. It is furthermore helpful that
because many HDTs involve approved drugs, often PK models are available
already to characterize their PK[94,95].
Immune Responses
Models describing the key immune response components, such as dynamics
of macrophage counts, cytokines, and CD4+ and CD8+ T lymphocytes are
essential for QSP models to study HDTs. Systems biology models
describing the host-Mtb interactions within the site of infection
(lungs)[56] have been previously developed, and later linked with
lymphatics[50] and blood circulations[96]. The states included
in these models were resting-, activated-, and critically
infected-macrophages, cytokines, such as IFN-γ, IL-10, and IL-12,
immature- and mature- dendritic cells, CD4+ lymphocytes, and intra- and
extra-cellular Mtb populations. The key feature of this model was
contributions of various immune components on intra- and extra-cellular
Mtb. The above-developed model was later expanded to include CD8+ cells
dynamics in lungs and lymph[49,97]. The parameters in these models
were identified from published human-derived or non-human primate (NHP)
experimental results or model fitting to in vitro or in vivo (mice)
data. These models can be expanded to include key drug targets involved
in Mtb HDTs and their downstream effects on functional immune response
changes and the quantitative interaction with Mtb bacteria.
To the best of our knowledge, there are currently no mathematical models
available in literature describing HDT-relevant pathways, such as
autophagy in Mtb infections; however, components and parameter estimates
from single cell systems biology models[98–103] can be adapted and
extended using experimental in vitro and in vivo data. For example, a
HDT model containing key biological features of autophagy[98]
including HDAC1-related components may be developed. The model
parameters can be informed using prior knowledge available in
literature[98] and data from in vitro experiments[40]. The model
may describe dynamics of the phagocytic cells and zebrafish infection
with Mm bacterial load overtime in HDAC1 inhibitors exposed macrophage
cell cultures as compared to controls, and this would allow estimation
of parameters relevant to HDAC1 effect. The simulations from the models
may be compared with the experimental outcomes, preferably from
different experimental conditions than the original experiments used for
parameter estimation. This allows validation of the model structure and
parameter estimates. In the above example, the simulations from the QSP
model including autophagy components may be validated against data from
zebrafish exposed to HDAC1 inhibitors (at various HDAC1 levels)
experiments[40].
Pathogen Dynamics
Models for the population dynamics of pathogens include the effect of
antimicrobial drug on the growth and inhibition-dynamics of Mtb bacteria
and emergence of treatment resistance. In vitro and in vivo kill dynamic
studies have enabled our understanding of parameters of Mtb growth
rates[18], bactericidal and bacteriostatic effects of conventional
anti-TB drugs[76], and resistance development rates of
bacteria[104,105]. Through the use of PK/PD modelling, dosing
strategies can be designed that optimize dosing schedules for maximal
bacterial control and reduced risk of resistance development. The
incorporation of immune cell effects on pathogen killing is a key
required step to study the effects of HDTs on Mtb treatment. Published
host-Mtb interaction models[50] can be updated to include
contributions of key HDT components on pathogen killing, as well as
pathogen evasion mechanisms. For example, an autophagy model may contain
quantitative relationship between bacterial load, mTOR, and autophagy.
This will allow evaluations and predictions of various mTOR inhibitors
on Mtb clearance by autophagy.
Implementation and Applications of the QSP Modelling
Framework
QSP modelling have successfully influenced various decision making
processes at different stages starting from discovery to late phase
development in various therapeutic areas[16] and offer potential for
the challenges faced in translation and design of HDT (combination)
treatments in Mtb infections. A QSP framework to translate and optimize
optimal HDTs should contain a combination of aforementioned model
components for PK of one or more (investigational) drugs, immune/host
response and pathogen dynamics, including their interactions. Depending
on the type of HDT drug studied, QSP models may be parametrized and/or
adapted in specific ways, e.g., to capture the drug-specific parameters
for PK, pathogen kill and immune system effects, and induction of
specific immune system effects. Various considerations and applications
of the HDT QSP modelling framework are discussed below.
Target Identification and Drug
Discovery
QSP models integrate various host-pathogen interactions and drug PK/PD
components; therefore, they can readily provide assessment of target
engagement upon stimulation or inhibition of certain target molecules at
various doses and affinities and its impact on overall treatment
outcome. This allows evaluations of the iterative process of hypotheses
generation, designing new experiments, hypotheses validation and/or
generation of new hypotheses. This approach can be applied to evaluate
known HDT targets and HDT candidate molecules, to discover new HDT
targets, or to discover and evaluate new HDT molecules. With advances in
technologies, applications of combining QSP modelling and machine
learning approaches to screen virtual drug compounds to enable discovery
of drugs with optimal PK/PD characteristics are being
evaluated[106].
Translational
Predictions
With increased complexity and innovation in design of new drugs within
the last two decades, mechanistic QSP models are increasingly being
applied to inform translation of the results across different
experimental conditions and species[107,108]. The systematic
incorporation of system-specific parameters not only for various
species, such as zebrafish, mice and humans, but also incorporation of
differences between in vitro systems and in vivo models, is crucial to
enable translation towards clinical HDT treatment designs[77,86]. In
some cases, i.e. for scaling from in vitro HFIM to humans, such scaling
is already well studied[76], whilst further studies are needed for
the host’s immune response components[109]. Consolidating
immune-relevant differences between preclinical models and
humans[109] may be challenging and resource intensive, as there are
varying strains of models used across different experiments depending on
the objectives of the experiments. On the other hand, the shown
evolutionary conservation of the metabolic responses to mycobacterial
infection in human patients and mice and zebrafish animal models show
that basic disease symptoms such as wasting syndrome are not depending
on species or varying strains[110]. Gene expression analysis data
across species may be used to inform parameters of expressions of genes
responsible for certain immune functions[111]. Such expression data
studies can be used to predict metabolism in a whole-genome metabolic
network theoretical modelling approach in various model organisms such
as zebrafish[112]. Factors such as state or severity of infection,
intensity of resistance, and sensitivity of drugs to bacterial strains
(for example between Mtb and Mm) may also be applied within the QSP
framework.
Variability and Precision
Medicine
The presentation and severity of TB is variable amongst patients, and
thus treatment responses, especially to HDTs, are variable. Many factors
such as age, sex, genotypes, co-morbid conditions (HIV, type 2 diabetes)
play role in determining the outcome of the disease and treatment. Thus,
considering these factors in the QSP framework is very important. For
example, known differences in PK and immune-response components for HIV
co-infected TB patients may be incorporated in the framework, and
extrapolate results from studies in TB patients to HIV-TB co-infected
patients[113]. Many PopPK models have evaluated these factors’
impact on variability in PK of conventional anti-TB drugs[114], and
thus can be included in QSP simulations framework. In addition to
external factors, considering immune-response relevant endotypes is also
important[115,116]. Technological advances within the last century
enabled generation of large-scale data, including omics data. The
large-scale omics data may enable us to better understand the
inter-individual variations associated with the parameters of the QSP
models[117,118]. For example, parameters, together with
inter-individual variations in them, describing the expression of
baseline state of immune response components within lymph nodes and
blood were estimated using data from a flow cytometry analysis of blood
leukocytes and genome-wide DNA genotyping from 1000 healthy
humans[117]. In addition, parameters, together with inter-individual
variations in them, describing fractions of various lymphocytes within
tumour microenvironment were informed using transcriptomics data from
cancer patients.[117] Gene expression analysis of omics datasets
from total of 443 TB patients enabled stratification of the patients
into two groups. One of the two groups was characterized by increased
gene activity score for inflammatory response and decreased gene
activity score for metabolism-relevant pathways, and patients in this
group showed slower time to negative TB culture conversion and poor
clinical outcome[115,116]. Similarly, gene expression data can be
used to include variability in the QSP models and inform outcome of
certain HDT treatment.
Selection of Optimal Dosing Regimens and Combination
Therapies
QSP models are also suitable to evaluate various combination therapies
with optimal dosing regimen efficiently and can be especially valuable
for difficult to treat diseases, such as TB. A QSP model enabled
simulations of multiple combination therapies and identified the most
effective dual-drug combination for the treatment of advanced
castration-resistant prostate cancer where effectiveness of
immunotherapy was previously insufficient [119]. In the TB disease
space, QSP modelling has recently been applied to predict patient
outcome with intensive dosing regimen and to explore shorter treatment
duration scenarios for conventional anti-TB drug therapy[18].
Overall, the use of QSP modelling can serve as a valuable tool to
efficiently design and develop HDTs for treatment of TB.