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13 • The Articial Intelligence Revolution 351
FIGURE13.4 MsAb quaternary structure dictates binding properties, which directly inuences biological activity. (a) Schematic representation highlighting the molecular basis of
the relationship between the format rearrangements of MsAbs (e.g., linker length) and
optimal biology. For example, in the case of T-cell engagers (and others), the location of the
targeting epitope on the TAA (tumor-associated antigen) (membrane proximal vs. distal) can
be critical for tumor cell killing. (b) A representation of the relationship between MsAbs’
valency and the target copy number and distribution on the surface of the cell.
352 Biopharmaceutical Informatics
13.4 ADAPTING AI TO THE DESIGN OF MULTISPECIFIC ANTIBODIES
13.4.1 Structure Prediction and Modeling
The advent of AI/ML technologies will bring signicant advancements to the design of
MsAbs, enabling precise predictions of optimal formats, binding afnities, and stability.
This will allow tailoring MsAb for specic indications and for each route of administra‑
tion (ROA). These tools can model the complementarity of epitopes, aiding in the stra‑
tegic placement of different antigen‑binding sites to enhance therapeutic efcacy while
minimizing the need for extensive screening. Despite increased complexity, AI‑driven
linker design promises optimal binding domain functionality and separation in mAbs,
as demonstrated in small molecules [44]. The effect of the linker and its composition
has been demonstrated for bispecic diabodies [45] and seen in single‑chain Fvs [46]
but has mostly been done using rational design of the linkers. This is crucial for ensur‑
ing that each binding arm of an MsAb can engage its target without interference from
adjacent arms.
13.4.2 Developability Prediction and Optimization
Often, the requirements in terms of stability and cost of goods for MsAbs are similar
to those of mAbs, which can be a tall order for these NMEs. Predicting and optimizing
the developability of MsAbs presents unique challenges, given their complexity. A more
suitable approach can be for the AI/ML models to rst predict the critical attributes of
each BB, including solubility, aggregation potential, thermostability, and yield, among
others, and then later to the MsAb entity as a whole. Despite the scarcity of data on
the combined properties that MsAbs can generate, AI‑driven approaches can leverage
existing knowledge from mAb development to inform predictions and optimizations.
In addition, LLMs for biology like PGLM [15] can offer an exciting opportunity to ll
in the gaps in the absence of prior knowledge and could have the potential to become
foundation models over time when more data is integrated into them. This holistic view
of developability ensures that the individual BBs and overall MsAb design meet the
necessary criteria for successful development and production. By understanding and
manipulating these molecular characteristics, AI‑driven methods streamline the design
process, reducing the complexity and resource requirements traditionally associated
with developing MsAbs.
13.4.3 Virtual Screening and Lead Identication
AI/ML technologies can revolutionize virtual screening processes, enabling the efcient
evaluation of vast libraries of antibody fragments. These tools can identify promising
13 • The Articial Intelligence Revolution 353
candidates by screening multiple structure complexes and optimizing the linkers that
connect different binding entities. Through structural analysis, AI algorithms deter‑
mine the best balance between exibility and rigidity of these linkers, ensuring optimal
orientation and functionality of the binding domains. This accelerates the discovery
phase, allowing researchers to focus on the most promising MsAbs candidates for fur‑
ther development.
Data‑driven design coupled with automation have revolutionized biologics devel‑
opment. By integrating the ‘Design‑Build‑Test‑Learn’ cycle with rigorous design of
experiments (DoE), researchers can leverage data to intelligently guide the generation
of novel and rapidly improved therapeutic candidates [47–49]. Data‑driven approaches
are rapidly becoming more sophisticated, enabling faster design cycles and the integra‑
tion of more parameters from both in silico and experimental assays with the conver‑
gence of experiments and algorithmic development. The integration of active learning
algorithms like Bayesian optimization accelerates biologics optimization [50], paving
the way for fully automatic protein engineering laboratories [51]. Indeed, elaborated
automation efforts are rapidly accelerating this process, generating vast datasets in real
time [52]. Robust data infrastructure, molecule registration for data curation, proper
DoE‑controlling repeats, references, and low‑signal to noise and algorithms capable of
identifying subtle patterns are crucial for translating data into actionable insights for
ML [53]. Design‑Build‑Test‑Learn cycles can be run in just under 2 weeks [41], enabling
fast improvements in the molecule design (Figure13.5).
13.4.4 In Silico Modeling and Simulation
AI/ML simulations are essential tools in MsAb design, offering insights into potential
off‑targets and efcacy. While the absence of off‑target effects observed in mAbs is
an essential pre‑condition, it does not guarantee the same for multispecic constructs;
therefore, careful modeling and real‑world validation remain crucial. Meticulous
screening of the human proteome with ML algorithms can predict off‑target interac‑
tions, minimizing unwanted side effects for small molecules [54]. Additionally, electro‑
static calculations can be used to predict specicity of mAbs, improving their precision
[55]. Furthermore, AI models facilitate the exploration of optimal geometries and afni‑
ties between binding arms as has been explored for mAbs using MD simulations [56],
ensuring that the MsAb can effectively engage multiple targets with high specicity and
ef cacy.
The complexity of determining optimal epitopes varies depending on the targets
and their localization, whether they are membrane‑bound or soluble where the complex‑
ity can increase as shown by a cytokine receptor using MD simulations [57]. These in
silico approaches offer a powerful means to predict and rene the therapeutic potential
of MsAbs before advancing to costly and time‑consuming experimental stages.
354 Biopharmaceutical Informatics
FIGURE 13.5 Optimizing biologics with the design-build-test-learn cycle. This iterative cycle is employed to optimize biologics such as mAbs using a data-driven approach.
Scientists begin by designing the initial sequence, carefully considering features like afnity,
yield, and stability that require improvement. The protein’s properties are then analyzed
to predict potential interactions and effectiveness. After this analysis, selected sequences
are built and experimentally tested for these crucial features. The resulting data is fed back
into an active learning loop, rening the design process in subsequent iterations. This cycle
enables the development of highly specic and effective biologics for use in disease treatment, diagnostics, and therapeutic delivery.
13 • The Articial Intelligence Revolution 355
13.5 THE FUTURE: BEYOND OPTIMIZATION
13.5.1 Market Trends and Commercialization
The advent of AI‑driven MsAb development marks a revolutionary shift in the biophar‑
maceutical industry, offering new therapeutic strategies for a range of diseases previously
deemed untreatable. Innovations in therapeutics for cancer, autoimmune disorders, and
rare diseases, exemplied by breakthrough treatments like blinatumomab [32], Hemlibra
[58], and Mim8 [48], underscore the transformative potential of MsAbs. These therapies
have expanded the horizon of treatable conditions, leveraging the unique capability of
MsAbs to address complex pathological mechanisms that single‑targeted therapies cannot.
AI/ML technologies stand at the forefront of this transformation, poised to signi‑
cantly increase the number of MsAbs by streamlining the development process. Through
in silico screening, these technologies can sift through vast libraries of candidate mol‑
ecules, a task that is impractical with traditional in vitro and in vivo methods due to the
sheer number of potential combinations (Figure13.3a). This computational approach not
only accelerates the identication of viable candidates but also enhances the quality of
leads, promising a future where MsAbs are developed faster, more cost effective, and
more importantly, a future where more MsAbs reach the clinic and help patients in need.
13.5.2 Logic Gates, Biosensors, and De Novo Design
The eld of logic gate‑based antibody design and de novo mAb design represents an
exciting frontier in antibody engineering. MsAbs introduce the possibility of embedding
computational intelligence within therapeutic molecules. For example, logic gates such
as AND, OR, NOR, NOT, XOR, and NAND can be engineered into MsAbs to rene
their targeting capabilities and reduce off‑target effects, thereby minimizing drug toxic‑
ity and increasing efcacy in the clinic [59] (Figure13.6).
Similarly, other technologies also known as molecular switches responsive to alter‑
ations in pH and adenosine triphosphate (ATP) levels, among other biophysical proper‑
ties, can also be engineered into MsAbs, which has been shown for mAbs [60,61]. This
sophisticated level of control enables the design of MsAbs that are activated only in
specic tissues or microenvironments, enhancing therapeutic precision and safety.
De novo design of antibodies using AI/ML offers huge potential. It allows for
the generation of mAbs with highly specic epitopes and precise cognate chain pair‑
ing, which is ideal for the integration into MsAb formats that require chain pairing.
However, other properties, including afnity and pharmacokinetics (PK)/pharmacody‑
namics (PD) characteristics, may still require further validation within the context of
MsAbs. These properties are often inuenced by quaternary structure [62]. Altogether,
AI algorithms are crucial in this endeavor, enabling the prediction of afnity, optimiz‑
ing molecule design for efcacy and safety, and generating sequences with enhanced
manufacturability and stability.
356 Biopharmaceutical Informatics
FIGURE13.6 Illustration of MsAb formats designed to deliver on AND and OR gate mechanisms of conditional activation. The T-cell engager MsAb molecule binds to two antigens
(Target X and Target Y) on the target cell (TAA), triggering a signaling pathway within the
T cell. In the AND gate format (a), T-cell activation requires binding to both Target X and
Target Y simultaneously. Alternatively, in the OR gate format (b), T-cell activation occurs if
the MsAb binds to either Target X or Target Y.
13.5.3 Challenges and Opportunities
Despite the promising advancements, the integration of AI/ML into the design and
engineering of MsAbs still presents several challenges. The optimal pairing of differ‑
ent BBs to achieve the desired therapeutic effect remains a complex task. However, the
capabilities of AI/ML in de novo design and developability prediction should offer a
pathway to overcome these obstacles. By rening sequences, e.g., removing liabilities
and precisely predicting interactions, these technologies expedite the development of
new therapies, signicantly reducing time to clinic.
However, the prospect of accelerating the development of MsAbs hinges on the
ability of AI/ML to rene and streamline the design process. As these technologies con‑
tinue to evolve, they will enable the creation of more effective, safer, and more targeted
therapies, opening new avenues for treating a wide range of diseases and delivering on
the promise of personalized medicine.
13.6 CONCLUSION
MsAbs hold the promise to revolutionize treatment options, addressing limitations faced
by mAbs. However, their complex structures demand advanced computational approaches
like AI/ML, which have the potential to revolutionize MsAb design. When AI/ML reaches
its full potential, it will dramatically accelerate the design and optimization of MsAbs.
Embracing this technology shift is crucial for unlocking the full clinical potential of MsAbs.
13 • The Articial Intelligence Revolution 357
ACKNOWLEDGMENTS
The authors are thankful to Dr.Sandeep Kumar’s invitation to contribute to this book
and for his additional guidance and review. Also, they extend their thanks to Leonard
Wossnig for pre‑reading and providing helpful suggestions.
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