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378 M. Karlberg et al.
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ligand binding of peptides, and Obrezanova et al. [3] used several amino acid scales
to predict mAb aggregation propensity based on the primary sequence. However,
even though amino acid descriptors explain the differences in the primary sequence,
they do not take into consideration potential interactions between the amino acids
in or between primary chains. It has been argued that this simplification can lead
to a loss of information concerning properties of secondary and tertiary structure in
larger proteins [25].
Descriptors can also be generated by using empirical equations on the entire primary sequence to infer protein properties such as the isoelectric point, hydrophobicity, molecular weight, physicochemical properties and secondary structure content,
to name a few. Many such tools and applications are available on bioinformatics
sites, such as Expert Protein Analysis System (ExPASy) [36] and European
Bioinformatics Institute at European Molecular Biology Laboratory (EMBL-EBI)
[37].
2.3 Homology Modelling and Molecular Dynamics for
Descriptor Generation
Descriptors capturing structural and surface properties can be generated by using
existing crystal or Nuclear Magnetic Resonance (NMR) structures or by building
models using homology modelling. The latter is performed by finding proteins with
existing 3D structures that have a high level of similarity to the primary sequence
of the protein of interest. These proteins are then used as templates to predict the
likely structure of the queried protein [38]. This has been successfully used in
many studies where information such as surface areas, angles and surface properties
was extracted [39–41]. The method is especially useful when no crystal structure
exists. Caution needs to be exercised, however, as the homology models are only
predicted structures. Breneman et al. [42] introduced a methodology for generating
2D surface descriptors, also called transferable atom equivalent (TAE) descriptors,
by reconstructing the electronic surface properties of the molecular structures from a
library of atomic charge density components. This has the advantage of representing
surface variations such as hydrophobicity and charge distributions numerically,
which is of great importance when studying, for example, protein binding to
an anion exchange chromatographic column packing using different salts [43].
Robinson et al. [44] used the TAE descriptors to relate the structural differences
between several Fab fragments to predict column performance between different
chromatographic systems. It has been argued, however, that caution needs to be
exercised when using library-based descriptors as these are usually directly related
to a specific state of a compound that was measured in a unique environment. This
means that these descriptors should only be applied if experiments were carried
out in an identical or in a similar environment. Otherwise, this might cause the
descriptors to be biased [24]. Other structural properties, such as molecular angles
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and solvent accessible surface areas extracted from homology models, were used by
Sydow et al. [41] to determine the risk of degradation of asparagine and aspartate
in mAbs as PTMs. Similarly, Sharma et al. [40] investigated the risk of oxidation of
surface accessible tryptophans.
Due to the flexibility and size of the mAbs, it is very difficult to produce good 3D
structures based on X-ray crystallography and NMR. Instead, homology modelling
has proven to be a good alternative to circumvent this problem. However, due to
the size and the many flexible parts in the mAbs, pure homology models might not
give a sufficiently accurate representation of the reality. Molecular dynamics (MD)
is a useful tool that can be used to minimise the energy of the entire protein and
to simulate the dynamics of the protein of interest in different environments [45].
MD simulations have also shown very high similarities in the internal dynamics of
mAbs when comparing the simulated results to those observed in reality [46]. It
is therefore recommended to apply MD simulation to all homology models before
descriptors are generated in order to mimic the environment of the samples that
are used in QSAR studies. However, implementation of MD simulations is still
computationally expensive and time-consuming, and its practical use is therefore
still limited. Alternatives, such as coarse-grained MD might therefore be preferable
where computational power is lacking, but it is computationally cheaper. In coarsegrained MD, groups of atoms, e.g. residue side chains, are simulated as a single
static structure or point. This in turn aids in drastically reducing the number of
atoms in the system that needs to be simulated [47]. It should be noted that smaller
structural fluctuations and dynamics will be lost when using coarse-grained MD,
which needs to be considered prior to developing the descriptors.
2.4 QSAR for Protein Behaviour Prediction
The QSAR framework has been applied to a diverse range of challenges where
structural properties of pharmaceuticals have been used directly for the prediction
of different process-related aspects such as the prediction of isotherm parameters
in ion-exchange chromatography [48], ligand-binding in ion-exchange chromatography under high salt concentrations [49], binding of proteins in ion-exchange
chromatography under different pH conditions [50], protein surface patch analysis
for the choice of purification methods [51], chromatographic separation of target
proteins from Host Cell Proteins (HCPs) [39], viscosity, clearance and stability
prediction for mAbs [40] and degradation prediction of asparagine and aspartate
in mAbs [41], to mention a few. This also showcases one of the main strengths
of the QSAR/QSAM framework with its ability to link structural features to
many different forms of prediction outputs. It is important to note, however, that
identical experiments must have been performed on different mAbs to compare
the differences in structure and their effect on the output. Equally important is that
sufficient excitation is present in the output data in order for the effects to be linked
to the corresponding structural feature [52].
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3 Model-Based Prediction of Developability
There have been significant advances in computational prediction methods, and they
are starting to become more common in process development [53]. As mentioned
by Zurdo et al. [4], the ability to predict product-related characteristics that
strongly relate to the QTPP and/or CQAs can greatly simplify process development,
especially in the early stages when the product or process knowledge is limited. The
implementation of QSAR in process-related areas, such as protein purification, has
been researched extensively [2, 44, 48–50, 54–57]. Though not all the mentioned
examples concern mAbs specifically, the outlined methodology used in the different
research articles is still applicable. Given the significant proportion of mAb development cost that is incurred during downstream processing, considerable advantages
can be gained by being able to predict the performance of chromatographic columns
and their effect on product quality early in the process development. In the case
of mAbs, much of the cost is incurred during the purification due to the strict
regulations surrounding clinical safety of the end product [58, 59]. Examples of
regulations for mAbs include the removal of harmful structural variants while
retaining the desired structure based on evidence from clinical trials. The removal of
contaminants, such as HCPs, DNA and viruses, is also necessary in order to avoid
undesired immune responses in patients. Thus, for therapeutic use, a mAb purity of
>99% is required in the final formulation [60]. Therefore, the integration of QSAR
into QbD is proposed based on the valuable insight that QSAR can provide in early
process development and is illustrated in Fig. 2, which also shows how the QbD
framework can add to and improve the QSAR modelling with addition of new data.
Two main approaches of integrating the QSAR framework into the QbD
paradigm can be considered. The first approach is by only using generated structural
descriptors for development of models able to predict protein behaviours [56]. The
method is, however, more constrained as it requires data generated from identical
experimental setups, and therefore identical PP settings, in order to satisfy the
assumption that the observed effect is caused only by the differences in structure
between the proteins. Therefore, models developed this way are better for assessing
the manufacturing feasibility and/or potential CQAs before starting the process
development.
The second approach is to use the PPs of interest, taken from previous mAb
processes to use directly in the model development by either (1) adding the PPs
together with the generated structural descriptors as inputs [61] or (2) structural
descriptors are calculated to be dependent on the PPs, meaning that the values of the
descriptors will change with changing values of the PPs [50]. The latter is easiest
done by generating descriptors from MD simulations where changes in the soluble
environment can be implemented. This, however, requires that data is gathered from
similar experimental setups where only the PPs of interest have been varied. This
would usually not be a problem when gathering historic data generated from the
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Fig. 2 Proposed integration of QSAR into QbD where the upper half illustrates the simplified
framework of QbD (red circles) and the lower half illustrates a simplified version of the QSAR
framework (black circles). Transfer of characterisation data from previous mAb processes can be
used directly for model development using QSAR. Depending on the purpose of the developed
QSAR model, it can be used to directly aid in assessing CQAs or provide insight into PPs and
ranges
QbD paradigm as it will often conform to experimental designs based on DoEs
where the experimental environment is strictly controlled. The added benefit of this
approach is that the developed model will be able to account for both the structural
differences and the impact from the studied PPs when predicting protein behaviour.
This can potentially have great value in process development of new mAbs as PP
ranges can be assessed in silico and therefore greatly aid in reducing the number of
needed experiments, seen as grey arrows in Fig. 2.
The methods described above provide a reference for further risk assessment
and characterisation to be performed in the QbD framework, as they provide
information such as the behaviour of the product in different scenarios and increase
the product understanding. As additional information from new mAb processes
becomes available, models can be improved by expanding the data sets used in the
model development. This in turn will aid in providing more accurate predictions
due to lowering the sparsity by incorporating more protein structures. Available
characterisation research studies can also be used as additional sources of data in
order to improve the models by expanding the data set for model development.
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4 Case Studies
In line with the approaches described above, we have developed a model identification and parametrisation workflow (see Fig. 3) in order to explore the capabilities of
advanced modelling approaches in predicting the behaviour of various mAbs during
purification.
The development of a hybrid QSAR-based model with a structured workflow
and clear evaluation metrics, with several optimisation steps, was described in
detail in Kizhedath et al. [2]. Furthermore, using cross reactivity (from CIC
data) or solubility/aggregation (from HIC data) as responses and physicochemical
characteristics (primary sequence and 3D structure) of mAbs as descriptors, the
QSAR models generated for different applicability domains allow for rapid earlystage screening and developability as summarised in the case studies below and
detailed in Kizhedath et al. [2] and Karlberg et al. [56].
4.1 Cross-Interaction Chromatography as CQA Screening
Method
As indicated earlier, the success of a new mAb product depends not only on its
efficacy and manufacturability but also on the lack of undesirable side effects and
interactions. Bailly et al. [1] provide a useful overview of a range of analytical methods that can be used during the sequence selection and product/process development
in order to evaluate important CQAs. One such method is the cross-interaction
chromatography used in early screening to detect undesirable interactions with other
mAbs [62].
Kizhedath et al. [2] detailed the development of a hybrid QSAR-based model
with a structured workflow and clear evaluation metrics, with several optimisation
steps, that could be beneficial for broader and more generic PLS modelling. Based
on the results and observations from this study, a structured selection of data sets
and variables demonstrated increased model performance and allowed for further
optimisation of these hybrid models. Furthermore, using cross reactivity (from CIC
data) as responses and physicochemical characteristics of mAbs as descriptors, the
QSAR models generated for different applicability domains allow for rapid earlystage screening and developability [2].
The generated descriptors captured the physicochemical properties of mAbs with
varying degrees of resolution (local to global; singular to cluster). These descriptors
from different data blocks were subjected to an exploratory analysis to study any
separation based on intrinsic properties such as light and heavy chain isotypes as
well as species type of mAbs that would then allow for selection of descriptor
sets that could be used for QSAR model development. Exploratory analysis of the
descriptor data was carried out using unsupervised pattern recognition methods
such as principal component analysis (PCA) to visualise any intrinsic property-
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Fig. 3 Hybrid model development workflow outlining the different steps involved in descriptor
generation, pre-treatment and variable reduction; model development followed by model evaluation and optimisation. PCA principal component analysis, GA genetic algorithm, RMSECV root
mean square error of cross-validation, RMSEP RMSE of prediction. (Adapted from Ref. [2])
based separation or clustering. The descriptor sets were assessed for the influence
of intrinsic properties that could hamper with predictive model development
that facilitates mAb developability. Based on the results, the descriptors of the
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hypervariable region were chosen for further model development. Furthermore, the
samples were divided into appropriate heavy chain, light chain and species type for
developing models that have a better-defined applicability domain.
These descriptors were then used for developing a QSAR model for the
prediction of cross-interaction chromatographic retention times. Primary sequencebased descriptors do not take into account interactions between amino acid residues
or the antibody-antigen and antibody-receptor interaction space. To address this, 3D
structures of mAbs werefirst generated via homology modelling and MDsimulation
upon which structural descriptors were generated for QSAR model development.
Similar to the primary descriptors, resolution and type of descriptors selected
were similar, i.e. local and cluster-based substructure data set with electronic and
charge-based descriptors were identified to be important. Most of these descriptors
of importance arise from the hypervariable regions of mAbs similar to primary
sequence-based descriptors [63].
A combination of sequence-based and structural descriptors was also utilised for
model development. The structural descriptors outweighed the primary sequencebased ones; however, the overall model performance was of lower quality than that
of the models developed individually using primary sequence-based descriptors and
structural descriptors, respectively. This implies that careful selection of variables
based on expert knowledge should be performed such that the descriptors selected
capture both structural and sequence-based aspects of functional characterisation
[63].
Modelling techniques, such as those that use categorical responses (partial least
squares discriminant analysis, PLS-DA), as well as nonlinear modelling methods,
such as support vector machines(SVM), were also briefly investigated in this project
(data not shown). The performance of PLS-DA models was poor mainly due to
class imbalance. Furthermore, the influence of expert knowledge is greater for these
models as defining the classes is arbitrary. With regard to the SVM-based models
with non-linear kernels, the increase in model complexity only led to similar model
performance as that of the PLS models. Interpretability is lower for complex models
such as those generated by such SVM for similar model performance.
4.2 Hydrophobic Interaction Chromatography Retention Time
Prediction
Another important experimental assay, highlighted by Bailly et al. [1], is hydrophobic interaction chromatography (HIC), which is used in the assessment of mAb
aggregation and solubility. HIC is also a common polishing step in downstream
purification of mAbs [62]. Thus, successful prediction of HIC retention times can
yield important information for risk assessment of the feasibility of a mAb candidate
to be manufactured. It can also provide insight into process characterisation of the
HIC polishing step.
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In a study of Karlberg et al. [56], a QSAR modelling workflow linking mAb
structures to corresponding HIC retention times was implemented where the necessary level of intramolecular interactions for successful prediction was explored.
More specifically, descriptors were generated from three potential sources: (1)
primary sequence, (2) 3D structure from homology modelling and (3) 3D structure
from 50 ns MD simulations. This in order to compare the predictive performance
of a model based on descriptors sets that containing residue-to-residue interactions
(3D structures) with a model based on descriptor sets without residue-to-residue
interactions (primary sequence). As described previously in the CIC case study, the
three descriptors sets were subjected to an exploratory data analysis with PCA prior
to model development to investigate the presence of intrinsic structural variation
originating from the light and heavy chains as well as the species type that could
potentially impact model performance.
All QSAR models were fitted according to the workflow outlined in Fig. 3. Here,
an SVM algorithm for regression was used with a linear kernel to allow for high
model interpretability [64]. The QSAR model developed on primary sequence-
based descriptors yielded suboptimal predictions of HIC retention times. This
resulted from a high model bias (under-fitting), meaning that critical information
was either missing or confounded in the generated descriptors and the resulting
model was therefore unable to correlate the mAb structure to the HIC retention
time. On the contrary, the QSAR model developed on descriptors generated from
3D homology structures was found to suffer from high variance (over-fitting).
This resulted from the fitting of non-informative noise present in the descriptor
set which resulted in poor model generalisation when evaluated on an external test
set. The main source for this was identified to be biased 3D structures originating
from the structure generation step with homology modelling. More specifically, the
generated 3D structures were found to be in unfavourable structural conformations
which resulted in biased descriptors that were unable to represent the true structural
properties of the mAbs. Instead, MD simulations were applied in order to allow
residue side chains and domains in the mAb homology structures to relax and attain
more energetically favourable conformations as seen in Fig. 4 [56].
The QSAR model developed using the resulting MD descriptors achieved much
higher performance in the evaluation of the external test set (R
the homology model-based descriptors (R
2
=−0.08). It was found that descriptors
2
=0.63) compared to
generated from the hypervariable regions, or more specifically the CDR loops
column binding where properties such as surface charge and hydrophobicity, were
especially important. This is consistent with previous research [65].
5 Conclusions
This chapter reviewed the opportunities and the challenges in model-based risk
assessment of mAb developability. A structured workflow was proposed for hybrid
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Fig. 4 Displacement of the VHdomain (blue arrow) and loops during the MD simulation of
eldelumab. The heavy chain and light chain are represented in blue and red, respectively
QSAR model development to assist in the evaluation of important mAb developability characteristics.
Two case studies highlighted the benefits of such a workflow and the use of
PLS and SVM models in predicting Cross Interaction Chromatography (CIC) and
Hydrophobic Interaction Chromatography (HIC) retention times as important mAb
characteristics related to risks of aggregation and non-specific binding. While some
positive observations were reported, further work exploring non-linear modelling
techniques, applying the modelling framework on larger and/or more industrial
data sets as well as newer types of mAbs such as bispecifics, and developing
QSAR models around other substructural and species-related applicability domains
of MAbs, including glycoform conformation as a feature for model development, to
name a few would provide robust basis for evaluation of this workflow. For example,
further investigation to ascertain the applicability of complex modelling techniques
such as artificial neural networks, random forests, and Bayesian models for early
stage screening of mAb therapeutics would be beneficial. Model predictability and
utility can be further improved with the inclusion of better mAb features such as
glycoform conformation and distribution, which are linked to mAb efficacy and
safety. Increasing the sample sizes within each substructure and specifying applicability domain could expand the applicability of this QSAR modelling framework.
Furthermore, this methodology could then extend to different therapeutic types such
as fusion proteins, bispecifics, single chain fragment variable and other novel mAbbased therapeutics.
There is a need for carefully designed predictive models to assess the efficacy
as well as toxicity of potential drug candidates at an early stage. A more effective,
high-throughput rapid screening of candidates based on adverse effects is required
at an early stage to filter out the number of candidates proceeding to clinical trials.
From a safety perspective, animal models are not representative of human
systems for assessing the efficacy and safety of biopharmaceuticals in specialised
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therapy areas like oncology and immunology. In this regard, computational toxicology tools like expert/hybrid systems provide a powerful complement during
design phases as they will allow for development of automated and reliable models
for predicting toxicity or adverse effect of monoclonal antibody therapeutics.
To make these predictive platforms more robust, descriptor calculation, feature
extraction, inclusion of pharmacokinetics and bioavailability characteristics, mechanistic understanding and multidisciplinary expert knowledge will be of paramount
importance. This could aid in reducing the number of lead candidates that could go
forward into the bioprocessing /manufacturing pipeline.
Thus, this would tackle two of the main setbacks biopharmaceutical industries
face today: manufacturing failure and attrition. This will pave the way for the
advancement of rapid bioprocess development strategies for faster development
of effective and safe biopharmaceuticals and may in fact change the face of
biopharmaceutical manufacturing as we see today.
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