[1]

[1]

[orcid=0000-0003-1512-6426] [2]

# Artificial Intelligence for In Silico Clinical Trials: A Review

Zifeng Wang<EMAIL_ADDRESS>Chufan Gao<EMAIL_ADDRESS>Lucas M.
Glass  Jimeng Sun<EMAIL_ADDRESS>Department of Computer Science,
University of Illinois Urbana-Champaign  Carle Illinois College of Medicine,
University of Illinois Urbana-Champaign  Analytics Center of Excellence, IQVIA

###### Abstract

A clinical trial is an essential step in drug development, which is often
costly and time-consuming. In silico trials are clinical trials conducted
digitally through simulation and modeling as an alternative to traditional
clinical trials. AI-enabled in silico trials can increase the case group size
by creating virtual cohorts as controls. In addition, it also enables
automation and optimization of trial design and predicts the trial success
rate. This article systematically reviews papers under three main topics:
clinical simulation, individualized predictive modeling, and computer-aided
trial design. We focus on how machine learning (ML) may be applied in these
applications. In particular, we present the machine learning problem
formulation and available data sources for each task. We end with discussing
the challenges and opportunities of AI for in silico trials in real-world
applications.

###### keywords:

Clinical trial In-silico trial Artificial intelligence Machine learning

11footnotetext: This author contributed equally to the first author.

##  1 Introduction

A clinical trial is a prospective study aiming to compare the effects and
value of new interventions in human subjects. The median cost of a clinical
trial was US$3.4 million for a phase I trial, $8.6 million for a phase II
trial, and $21.4 million for a phase III trial [1]. The high cost comes from
different activities such as protocol design, site identification, participant
recruitment, treatment, and follow-up tracking. It is of broad interest to
reduce the time and cost of drug development and minimize the risks of adverse
side effects.

In silico trials, also known as virtual trials, are clinical trials conducted
digitally, in contrast to in vivo trials conducted in living organisms such as
the human body. One of the capabilities of in silico trials is to develop
individualized virtual cohorts for testing the safety and efficacy of new
treatments through computer simulation, and modeling [2]. In silico trials may
also assist the design of clinical trials, thus facilitating more efficient
trial execution. While other industries, such as manufacturing industries,
have long used computer simulations to accelerate research and development for
cars and buildings, the pharmaceutical industry has only recently started
exploring in silico methods [3]. This is probably due to the rapid growth of
efficient, accurate, and reliable computational models and artificial
intelligence (AI).

Interest in AI for in silico trials has grown for two primary reasons. First,
the rising prevalence of electronic health records (EHRs) and electronic data
capturing systems (EDC) have produced an unprecedented amount of patient data
intractable for manual processing and analysis. In contrast, AI and machine
learning (ML) models can take full advantage of patient data, automatically
processing and mining patterns from such data. Second, AI has the potential to
provide novel insights into many previously infeasible tasks, e.g., simulating
digital twins for testing treatment efficacy. In light of this, this article
summarizes the recent AI applications to in silico clinical trials and
describes associated challenges and opportunities.

Fig. 1 positions in silico trials in the context of the entire pharmaceutical
pipeline. In general, designing a drug undergoes two phases: drug discovery
and drug development, before the new drug is commercialized into practice. In
the discovery phase, compounds are first tested to modulate the activity of
the biological target of interest through molecule screening. Then, the
selected compounds will be optimized into lead compounds through molecule
optimization and will be tested in animal models through preclinical studies.
After that, the drug development phase begins with progressing the candidate
drugs to approval through multi-phase human trials. Four phases of clinical
trials can be performed in this stage, where in silico trials are
instrumental. The data regarding the safety and efficacy of candidate drugs
for human beings will be collected to support the approval for
commercialization. Upon approval, post-market surveillance on adverse effects
across a large population can be conducted to monitor the safety of the
approved drugs. Pharmaceutical companies also market new drugs to clinics
through drug recommendation and consider drug repurposing to find new
indications for existing drugs.

Several reviews of molecule design and genomics have been published [4, 5],
which lie in the drug discovery space. There are also reviews of AI in
clinical decision support and healthcare applications [6, 7]. However, we
focus on the emerging area of in silico trials in the drug development stage.
The most relevant works are Pappalardo et al. [2], which introduced the
advances of in silico trials, and Weissler et al. [8], which reviewed machine
learning for general clinical research. However, we study AI tasks in the
context of in silico trials, which are not covered in previous works.

In particular, we categorize AI applications of in silico trials into three
main topics as shown by Fig. 1:

  1. 1.

Clinical simulation is about creating virtual cohorts or organisms to study
the efficacy and safety of treatments.

  2. 2.

Individualized predictive modeling is about building predictive models that
adapt to personal characteristics to monitor and improve patient outcomes.

  3. 3.

Computer-aided trial design leverages ML and AI to organize, evaluate, and
improve clinical trial protocols.

We list the main technical papers mentioned in this article in Table 1, which
will be discussed in later sections. In addition, we also present the machine
learning formulations and available data sources for each task. Since this
review focuses on in silico trials, we will not cover how machine learning is
used for other tasks in clinical trials, e.g., participant recruitment [9],
decentralized trials [10], and trial site selection [11].

Figure 1: Organization and coverage of this survey. This survey focuses on AI
for clinical trials, emphasizing in silico clinical trials. We review a series
of hot topics and then provide ML formulation and available data sources
correspondingly. At last, we identify challenges and opportunities in this
domain.  Table 1: A list of papers related to AI and in silico clinical
trials.

Topic | Task | Reference | Method
---|---|---|---
Clinical Simulation | Digital Twins | Walsh et al. [3] | Conditional Restricted Boltzmann Machines [t]
Bertolini et al. [12] | Conditional Restricted Boltzmann Machines
Bertolini et al. [13] | Conditional Restricted Boltzmann Machines
Choi et al. [14] | Generative Adversarial Networks
Guan et al. [15] | Generative Adversarial Networks
Cui et al. [16] | Generative Adversarial Networks
Baowaly et al. [17] | Wasserstein Generative Adversarial Networks
Zhang et al. [18] | Wasserstein Generative Adversarial Networks
Biswal et al. [19] | Variational Auto-encoders
Lee et al. [20] | Auto-encoders and Generative Adversarial Networks
Xu et al. [21] | Conditional Generative Adversarial Networks [b]
Biosimulation | Eddy and Schlessinger [22] | Differential Equations [t]
Kovatchev et al. [23] | Differential Equations
Man et al. [24] | Differential Equations
Gillette et al. [25] | Differential Equations
Compte et al. [26] | Differential Equations
Baillargeon et al. [27] | Differential Equations
Zhang et al. [28] | 3D Printing
Low et al. [29] | 3D Printing
Herland et al. [30] | 3D Printing
Yu et al. [31] | Reinforcement Learning [b]
Individualized Predictive Modeling | Risk Detection | Rajpurkar et al. [32] | Gradient-boosted Decision Trees [t]
Wang and Sun [33] | Transformers
Wang and Sun [34] | Transformers
Baumel et al. [35] | Convolutional Networks
Cheng et al. [36] | Temporal Convolutional Networks
Ma et al. [37] | Temporal Convolutional Networks
Kam and Kim [38] | Multi-layer Perceptron
Choi et al. [39] | Multi-layer Perceptron
Rajkomar et al. [40] |  Recurrent Neural Networks
Che et al. [41] |  Recurrent Neural Networks
Xu et al. [42] |  Recurrent Neural Networks
Gao et al. [43] |  Recurrent Neural Networks and Convolutional Networks
Wang et al. [44] | Graph Neural Networks [b]
Sequential Event Prediction | Choi et al. [45] |  Recurrent Neural Networks [t]
Pham et al. [46] |  Recurrent Neural Networks
Ma et al. [47] |  Recurrent Neural Networks
Ma et al. [48] |  Recurrent Neural Networks
Liu et al. [49] |  Transformers [b]
Computer-aided Trial Design | Trial Semantic Parsing | Lehman et al. [50] |  Recurrent Neural Networks [t]
Marshall et al. [51] |  Recurrent Neural Networks and Language Models
Nye et al. [52] |  Recurrent Neural Networks and Language Models
Wang and Sun [53] |  Language Models
Wang et al. [54] |  Sequence-to-sequence Networks
Yu et al. [55] |  Sequence-to-sequence Networks [b]
Trial Outcome Prediction | Gayvert et al. [56] |  Random Forests [t]
Hong et al. [57] | Gradient-boosted Decision Trees
Qi and Tang [58] |  Recurrent Neural Networks
Siah et al. [59] |  Gradient-boosted Decision Trees
| Fu et al. [60] |  Graph Neural Networks and Language Models [b]
Trial Protocol Evaluation | Liu et al. [61] |  Language Models [t]
Liu et al. [62] |  Shapley Scores and Cox Proportional Hazard Model
Xu et al. [63] |  Poisson Factor Analysis
Rogers et al. [64] |  K-means Clustering and Cox Proportional Hazard Model [b]

##  2 Clinical Simulation

One of the most expensive aspects of clinical trials is testing. It usually
includes in vivo experiments like whole microscopic human tissue cultures,
which may raise ethical issues. In silico trials are an ongoing research area
aimed at using simulations or machine models to replicate some, if not all,
parts of a regular clinical trial.

###  2.1 Digital Twins

Figure 2:  Part A shows an example of Digital Twins: real patient data is used
to train a generative neural network for simulating in silico patients
(digital twins). See §2.1. Part B shows an example ML model learning a control
algorithm for diabetes by interacting with a biosimulator such as an
artificial pancreas, see §2.2 for details.

One of the primary benefits of in silico clinical trials is the capacity to
run experiments in a virtual environment without the need for expensive and
time-consuming real-world experiments. One technology being heavily discussed
in the clinical trial space is the idea of a digital twin – an artificial
physiological model of a patient that responds to various clinical trial
experiments [65, 66].

The first concept of a digital twin was introduced by National Aeronautics and
Space Administration (NASA) through their Apollo 13 mission, which used a
simulation of the actual spacecraft to test navigational strategies, inspiring
the concept of a "twin strategy" [67]. This allowed NASA to guide the
astronauts to safety despite the crisis. However, digital twins are a
relatively novel idea in the clinical space. While there has been much work in
the form of position papers promoting the general concept of digital twins
[68, 69, 70, 71, 66], only a few have described their methodology in detail.

A concrete example includes the use of digital twins to model the progression
of Multiple Sclerosis (MS) by Walsh et al. [3], which utilizes Conditional
Restricted Boltzmann Machines (CRBMs) [72]. They showed that digital subjects
generated by the CRBM were statistically indistinguishable from actual
subjects enrolled in the placebo arms of clinical trials for MS. Similarly,
Bertolini et al. [12] described a generative model composed of two CRBMs that
accurately simulated subjects with Mild Cognitive Impairment (MCI) and
Alzheimer’s Disease (AD). They showed that linear classifiers were unable to
distinguish actual subjects from their digital twins based on the generated
features; Bertolini et al. [13] described a similar technique on the
Amyotrophic Lateral Sclerosis (ALS) that demonstrated how integrating CRBM
digital twins into clinical trial designs could reduce the sample size
required to achieve the desired power. Fig. 2 illustrates how digital twins
are created from subjects.

Another closely related research area is synthetic patient data generation.
This research area seeks to simulate patient records, usually for specific
disease conditions. For example, one can predict future patient EHR data using
current patient baseline metrics or historical EHR data. Early works on
patient record generation methods used rule-based methods [73, 74, 75].
However, the rule-based methods cannot provide realistic and complex patient
data to support general machine learning (ML) tasks. In contrast, deep
generative models advanced by the power of neural networks, e.g., variational
auto-encoders (VAE) [76], generative adversarial networks (GAN) [77], gain
attention of researchers. Choi et al. [14] introduced MedGAN, pioneering GANs
for discrete patient records generation. Multiple works subsequently worked to
improve GANs for EHRs generation. Guan et al. [15] introduced GANs for
generating medical texts instead of just structured EHR data. Baowaly et al.
[17], Zhang et al. [18] tested multiple GAN architectures and loss functions,
like Wasserstein GAN with gradient penalty [78] and boundary-seeking GAN [79].
Other works used other generative models such as VAE, which can produce
realistic data that improve downstream model accuracy when added to the
training set [19]. In some scenarios, a hybrid of GANs and other models can
yield superior performances. For example, Lee et al. [20] proposed dual
adversarial autoencoder (DAAE), which learns set-valued sequences of medical
entities, by combining a recurrent autoencoder with two GANs, adversarially
learning both the continuous latent distribution and the discrete data
distribution. This is to address the issue of mode collapse in GANs, where
GANs only generate a portion of the valid possible data. Cui et al. [16]
proposed Complementary pattern Augmentation (CONAN), which uses GANs,
adversarial training, and max-margin classification to improve rare disease
detection from EHR data. Biswal et al. [80] proposes a multi-modal GAN-based
generation method with self-supervised learning to generate both X-ray images
and associated clinical notes.

Machine learning formulation. For unconditional generation (not specifying any
disease condition), the ML model tries to generate patient records from
scratch, usually taking random noise as inputs. For conditional generation
(specific disease conditions are given), the inputs can be the historical
records or key disease phenotypes in the form of medical codes. The generator
will try to produce patient records that match the specified input condition.
More formally, let $h^{k}$ be the EHR for patient $k$. An EHR generation task
or a digital twin cohort may be specified as generating a synthetic patient
based on generative model $p()$ trained on real patients $h^{1}$ to $h^{k}$:
$h^{synthetic}\sim p(h^{synthetic}|h^{1},h^{2},\dots h^{k})$.

Data sources. Real EHRs are available from MIMIC-III, IV, and eICU for
learning AI models that are capable of making synthetic EHRs generation on
variant conditions. Project data sphere provides access to patient-level trial
records for over 200 clinical trials.

###  2.2 Biosimulations

Mathematical or physical simulations of physiology are a subarea of
simulation. Rather than using ML methods, these approaches focus on systems of
differential equations, physics simulations, and other traditional methods
such as systems of equations. Biosimulations can have several benefits, such
as being more interpretable than black box ML methods. However, ML still has a
large part to play in this area. Since many of the aforementioned approaches
rely on concrete, mathematical and physical simulations of physiology, ML can
help tune model parameters for better accuracy. For example, in the case of
virtual trials for glycaemic control, there are still parameters that need to
be inferred for both the control algorithm and the simulation model. Black box
ML models such as Bayesian optimization may be helpful in such cases.

For example, the artificial pancreas is a popular area of research for in
silico closed-loop control of blood glucose in diabetes. Usually, the process
consists of a glucose sensor and a control algorithm for the delivery of
insulin [81]. Another example of this is Archimedes, which is a trial-
validated model of diabetes using differential equations of more than 100
interacting features [22]. Independent validation on 18 other real-world
trials showed that Archimedes is a realistic representation of the anatomy and
physiological responses of diabetes [82]. Compte et al. [26] designed an
insulin controller developed it on in silico trial simulations on a
mathematical system model [83]. These models have been validated using real-
world retrospective hospital control data [84]. Other relevant works on
artificial pancreas include an FDA-approved computer simulation of glucose
sensing, and insulin delivery [23], improvements on existing simulators [24],
and improvements on the control algorithm using modern ML methods like
reinforcement learning [31]. Furthermore, virtual trials for glycaemic control
have also been shown to be generalizable across hospitals worldwide [85].

Other system models include mathematical models of the heart fitted to
simulate cardiac electrophysiology from ECGs time-series [25]; modeling the
dynamics of human liver [86], a simulator for cardiac excitation and
contraction in the human heart via a classical resistance-based Windkessel
model [87] called the the Living Heart Project [27].

Machine learning formulation. To use ML models to develop control algorithms
for the biosimulators, techniques like reinforcement learning may be used. For
example, the input observations at state $s\in S$ could be the state of the
organ, given by its features such as glucose level; the ML model output would
be an action $a\in A$ to control biosimulator. For example, the available
actions could include how much insulin to give. Let $r$ denote the reward
corresponding to the patient’s health status.

More formally, a given trajectory would look like:

$s_{0},a_{0},r_{0},s_{1},a_{1},r_{1},s_{2},a_{2},r_{2},\dots$

Let $\gamma$ be the discount. We would then like to maximize the expected
discounted return at time $t$:

${G_{t}}\dot{=}\sum_{k=0}^{\infty}\gamma^{k}r_{t+k+1}$

Solving this optimization will yield a learned optimal policy function
$\pi(s)\xrightarrow{}a$ that learns what actions to take at a given state.

Data sources. In this case, the biosimulations themselves often act as a
simulation environment to enable downstream machine learning tasks. Archimedes
[22] is one source of a trial-validated model of diabetes, which may produce
pathophysiology of diabetes at a high level of clinical detail. The Living
Heart Project [27] is another source of data from an integrative, predictive
model of the living human heart. Both simulators are validated by selected
clinical trials [84] or by clinical observations from other studies [88].

##  3 Individualized Predictive Modeling

During clinical trials, predictive modeling catches abnormalities, e.g.,
alarming responses or patient in-adherence, that would suggest the alteration
of predefined treatment options. Additionally, depending on the data, early
termination of the trial should be considered if the interventions appear
harmful. If conclusive results are obtained, the trial may be stopped early
and moved onto the next appropriate stage [89, Chapter 16]. Moreover,
traditional trials only verify the average efficacy and safety among trial
participants rather than individual subjects. They do not adapt to the
characteristics of participants. The emergence of AI can potentially address
this personalization challenge through patient predictive modeling. This
section reviews how AI can mine through patient records and identify patterns
or make individualized predictions for response metrics, as shown in Fig. 3.

In clinical trials, AI has achieved individualized patient outcome predictions
for several diseases. For example, Rajpurkar et al. [32] leveraged gradient
boosted decision trees to predict the improvement of depressive symptoms for
patients receiving antidepressant treatment based on pre-treatment symptom
scores and electroencephalographic measures; Hong et al. [57] predicted
clinical drug toxicity with drug properties and target property features
through an ensemble of classifiers; de Jong et al. [90] built a model to
predict drug response for neurological disorder patients integrated with
genetics data; Wang and Sun [33] modeled and predicted the survival rate of
breast oncology patients using transformers [91]; Wang and Sun [34] proposed a
transferable transformer model that learned from data across multiple oncology
trials to boost mortality predictions for each trial.

The literature on outcome prediction using EHR data has also been fruitful.
EHRs usually contain demographic information, e.g., age and gender, and a
sequence of clinical visits, e.g., diagnoses, procedures, lab tests, and
medication prescriptions. Two main categories of predictive modeling tasks are
pertinent to the case: risk detection and sequential prediction of clinical
events [6].

Figure 3: Task illustration for the topic “Individualized Predictive
Modeling”. (A) Multi-modal data collected from various sources is aggregated
as the inputs for predictive models for predefined targets, e.g., mortality or
disease onset (§3.1); (B) A model iteratively predicts the events happened in
the next visit taking historical visits as inputs. (§3.2).

###  3.1 Risk Detection

Risk detection seeks to predict the risk of the target event, e.g., disease
onset, taking EHRs as inputs. The risk detection is formed in three types:
First, as either binary classification (e.g., onset of a specific disease [36,
38, 92] or mortality [40]); Second, as multi-class classification (e.g.,
disease stage prediction [41] and disease categories [44]); Third, as multi-
label classification (e.g., diagnosis code assignment [35]). Longitudinal
patient data such as EHRs are often used as input for risk detection. For
example, Kam and Kim [38] applied long short term memory (LSTM) model to the
vital signals drawn from the MIMIC-III data [93] for sepsis detection; Ma et
al. [37] designed a hybrid Recurrent Neural Network (RNN) and Convolutional
Neural Network (CNN) model that modeled heterogeneous EHRs to make health risk
predictions; Choi et al. [39] learned embeddings based on the multilevel
hierarchy of medical codes in EHRs to make heart failure predictions; Xu et
al. [42] processed multimodal physiological monitoring data, e.g., heart rate,
pulse, electrocardiogram, then fused them with clinical events as input for
LSTM to predict decompensation risk and length of ICU stay; Gao et al. [43]
accounted for disease progression stage using an LSTM and CNN to make
decompensation risk and mortality risk prediction for the end-stage renal
disease.

Machine learning formulation. The formulation is as follows: specify the
target response to be predicted, e.g., mortality, readmission, length of stay,
transform the input EHRs to either static descriptive features or sequential
event features, then predict the target using the EHR features.

Data sources. MIMIC-III and IV are the two public datasets that offer
comprehensive EHRs from ICU stays. Several risk prediction tasks can be built,
including decompensation prediction, mortality prediction, and readmission
prediction. Project data sphere provides access to patient-level trial records
for over 200 clinical trials upon application. SEER offers oncology incidence
data across the United States, which can be used for mortality risk prediction
and time-to-event prediction.

###  3.2 Sequential Event Prediction

Besides detecting specific diseases, we also observe AI’s use for predicting
other events from longitudinal EHR data. While some of the disease detection
works are capable of predicting events like readmission, they need to be
performed in two separate tasks, i.e., one model for disease detection and the
other for readmission prediction [40]. In contrast, sequential event
prediction seeks to make a multi-label classification for multiple events at
once. For instance, Choi et al. [45] adopt recurrent neural networks (RNNs) on
longitudinal EHRs to make multi-label predictions on diagnosis, procedures,
and medications; Pham et al. [46] leveraged LSTM to model the longitudinal
illness state to predict disease progression in time order; Ma et al. [47]
adapted attention mechanisms and RNNs for interpretable medical codes
prediction; Liu et al. [49] treated input EHRs as multimodal time series,
building a time-aware cross-event attention model to capture the correlations
among events and unify the modeling of heterogeneous EHRs (including
medication, diagnosis, procedure, and lab tests).

Machine learning formulation. The inputs are the historical events
(longitudinal EHR); the output consists of stepwise, multi-label predictions
for multiple target events.

Data sources. The most common data for sequential prediction are longitudinal
EHR data with multiple visits per patient. For example, MIMIC-III and IV
provide real longitudinal EHRs that fit the sequential event prediction task,
e.g., automatic coding and drug recommendation. Synthetic EHRs like SynPUFs is
another dataset commonly used for testing AI models for sequential prediction.
Project data sphere contains sequential clinical trial data that can be used
to predict trial adverse events and mortality.

Figure 4: Task illustration for the topic “computer-aided trial design”. (A)
An encoder encodes trial documents to embeddings, which are used for trial
retrieval or extraction of key entities (§4.1); (B) A model predicts whether a
clinical trial will succeed or fail given multisource real-world data (§4.2);
(C) Raw trial protocols, e.g., criteria, are parsed. A predictor is used to
evaluate how primary metrics change if any trial elements change (§4.3).

##  4 Computer-aided Trial Design

The clinical trial design plays a central role in the success of the clinical
trial. Researchers need to design trial protocols to answer scientific
questions, including the eligibility criteria, population size, duration, etc.
Historically, the trial design completely relies on the experiences of
clinical trial planners. As a result, some protocols like eligibility criteria
are far from optimal and may harm trial success [94]. By contrast, AI
algorithms may help automated trial design, as AI has been demonstrated in
drug discovery such as molecule design [95]. As such, AI for trial protocol
optimization has been quickly growing in recent years [96]. AI models can
extract structured information from trial documents [55], predict the trial
outcomes with multimodal trial-related real-world data (RWD) [60], and
evaluate how each component of the trial protocol influences the trial
outcomes [62]. Fig. 4 shows these examples.

###  4.1 Trial semantic parsing

Real-world data such as clinical trial protocols or scientific publications
about clinical trial results are crucial for facilitating trial design.
However, trial records are semi-structured documents with both unstructured
texts and structured result tables. Most AI models cannot handle such complex
semi-structured data before they are transformed into structured forms such as
SQL queries. For instance, Wang et al. [54] extracted medical queries from
MIMIC-III and generated SQL using neural networks; Yu et al. [55] focused on
SQL generation for clinical eligibility criteria. They introduce a new dataset
that includes the first-of-its-kind eligibility-criteria corpus and the
corresponding queries. It enables training models that translate the
eligibility criteria to executable SQL queries.

Evidence-based medicine underpins new trial design decisions, but clinical
trial results are disseminated as scientific papers in free texts. One way to
extract relevant information from these free texts is through evidence
synthesis, which automatically extracts Population, Interventions/Comparators,
and Outcomes (PICO) sentences from the scientific articles. For example,
Marshall et al. [51] developed RobotViewer, a model that integrates ML and NLP
to extract texts describing key trial characteristics as summary reports;
Lehman et al. [50] used a suite of ML models to extract the reported outcomes
and infer the treatment efficacy from scientific corpus; Nye et al. [52] built
RobotViewer to support the synthesis of evidence maps extracted from multiple
trials.

Another line of research aims to parse and formalize free-text eligibility
criteria so that the logic and properties represented by trial criteria can be
utilized to match patients to trials [97]. By parsing and extracting evidence
from trial documents, one can structure trials by standard concepts and the
corresponding values, which paves the way for predictive AI, e.g., tabular
learning [34], and document embedding [53]. A related task is to develop ML
models to match patient records to trial eligibility criteria descriptions
automatically [98, 99, 100].

Machine learning formulation. Two machine learning tasks are associated with
this scenario: (1) Given a free-text or semi-structured trial document,
extract the key entities (e.g., PICO) which capture the essential information
in a clinical trial; (2) Given trial eligibility criteria represented by free
texts, formalize each criterion to be machine-readable logics, e.g., SQL; (3)
Given patient records and free text eligibility criteria, automatically find
the patients that match the eligibility criteria.

Data sources. Raw trial eligibility criteria are available on
ClinicalTrials.gov. Yu et al. [55] released Criteria2SQL, an annotated dataset
with paired eligibility criteria and SQL queries, which makes it possible to
train AI models to translate from natural language criteria to the
corresponding logic forms.

###  4.2 Trial outcome prediction

The cost for bringing a molecule to market can take billions of dollars [1].
The process can take many years and yield a success rate of as low as 13% from
phase I to market [101]. Predicting the trial outcomes is thus crucial for the
economic consideration of drug development. We can avoid running clinical
trials that are highly probable to fail. Stakeholders can save unnecessary
funding/time from those trials that doom to fail, reinvesting in other
promising trials or re-designing trials to improve the chance of success.

The emergence of AI sheds light on leveraging multi-modal RWD for accurate
trial outcome predictions at the planning stage. Practitioners can iterate the
trial plan referring to the predictions made by AI until no better change can
be made. Gayvert et al. [56] used chemical structure and properties of drugs
and targets and a random forest model to predict the toxicity of clinical
drugs; Qi and Tang [58] employed RNNs to model both static and time-variant
variables to predict the phase III trial outcomes using patient records from
previous phases; Siah et al. [59] predicted the drug approval on both the drug
features and trial features using statistic ML methods; Fu et al. [60] further
fused multi-source RWD, e.g., drug molecules, disease ontology and trial
eligibility criteria, to support outcome predictions for all phases of trials.

Machine learning formulation. Given trial design protocols and the
corresponding multi-modal RWD, e.g., molecule structures, drug-drug
interaction graphs, and disease ontologies, predict the outcomes for trials
from all phases.

Data sources. ClinicalTrials.gov is a comprehensive open database that
contains clinical trial protocols and their status. There are over 400K
research studies in total. Researchers may process the raw protocols and
formulate a supervised trial outcome prediction task of the trial status as
the target. Additional RWD including drug molecules (e.g., DrugBank and
MoleculeNet) and ontologies like ICD codes (ICD-10), drugs (ATC codes), and
diseases (CCS-10) are beneficial to make better trial outcome predictions.
Siah et al. [59] created a challenge for drug approval prediction which
includes 10K phase II clinical trials. Fu et al. [60] created a trial outcome
prediction benchmark which includes many small molecule trials.

###  4.3 Trial protocol evaluation

Trial outcome predictors provide an overall success probability, given
clinical trial information. However, how exactly to improve these trials is
still an elusive question. In general, researchers may dive into eligibility
criteria to investigate if they are too stringent to recruit sufficient
patients [102] or too relaxed, reducing toxicity and adverse events [103].
This area of research yields a plethora of works in ML for data-driven
eligibility criteria design. For instance, Liu et al. [61] extracted key
entities from trial criteria using NLP to match patients records to expand the
potential cohort size; Xu et al. [63] derived subgroups of patient cohort with
clustering to identify the ones with less severe adverse events, which can
inform the design of criteria; Kim et al. [104] manually adjusted criteria to
broaden patient recruitment and assess the criteria influence on the inclusion
and exclusion groups; Liu et al. [62] utilized Shapley scores [105] to
estimate the change of hazard ratio of the included oncology patients when
removing each individual criterion; Rogers et al. [64] simulated a set of
criteria and assessed the correspondingly retrieved cohort in terms of the
hazard ratio, finding that it is possible to identify a group of superior
criteria combinations via k-means clustering on the count of cohort and hazard
ratio.

Existing methods quantitatively estimate the contribution of individual
criteria to the trial generalizability or further consider the adverse events
and hazard rate of the included cohort. Modifications such as removing one
criterion can be made based on the analysis. However, they cannot capture the
correlations between criteria, i.e., the effect of removing criterion A and B
simultaneously usually does not equal the summation of independently removing
A and B, respectively.

Machine learning formulation. Given a set of trial eligibility criteria
$\\{e_{1},e_{2},\dots,e_{m}\\}$ and a group of candidate participants
$\\{x_{1},\dots,x_{n}\\}$ where $x$ represents the individual historical
healthcare records in table or in sequence form, estimate the effect to the
target of interest by varying the matched cohort size $S$, removing criterion
$e_{1}$ or modifying $e_{1}$ to $\tilde{e}_{1}$, etc.

Data sources. Researchers may match trial records on Project data sphere to
the trials on ClinicalTrials.gov to retrieve the corresponding trial designs.
Simulation is then feasibly used to evaluate the trial protocols.

##  5 Discussion & Conclusion

In silico trials are virtual trials that conduct all and parts of clinical
trials digitally. Thanks to the potential economic benefit, researchers and
practitioners are excited about the applications of in silico trials. This
paper reviews different topics related to in silico trials, including clinical
simulation §2, individualized predictive modeling §3, and computer-aided trial
design §4. Next, we summarize the challenges and opportunities of AI for in
silico trials:

  * •

Lack of large-scale datasets and benchmarks. It is well known that the current
AI models rely heavily on high-quality labeled training data. There exist
limited benchmark data for trial-related tasks. Only a small fraction of
clinical trial records are available for application. Creating and releasing
benchmark data for various in silico trial tasks can encourage further work in
the domain. The only noticeable exception is the TOP benchmark for trial
outcome prediction [60].

  * •

Out-of-distribution (OOD) prediction. Most clinical trials are performed for
common diseases, and these AI models often cannot generalize to rare diseases
due to data distribution shifts and small sample sizes. It is challenging to
achieve the descent OOD performance for new unseen trials using AI models.

  * •

Inference in low-data scenarios. Despite the current success of AI models in
healthcare, AI for clinical trials often suffer from small sample sizes. Most
clinical trials have less than a thousand participants, with most phase I
trial having less than a hundred participants. This poses a major challenge
for learning powerful AI models for clinical trials. Transfer learning is a
promising direction to allow AI models to apply across clinical trials.

  * •

Federated learning across organizations. One approach to tackling the small
sample size problem in a single clinical trial is to train AI models using
data from multiple trials. However, most trials have unique designs and
collect different measurements inconsistent across trials. Plus, clinical
trial data are sensitive and difficult to share across organizations due to
privacy and legal constraints. Federated learning provides a promising
framework to support AI model building across organizations without direct
data sharing with small initial success in healthcare application [106].
However, due to heterogeneity in trial data, federated learning is still
impractical to use for clinical trial applications.

Finally, in silico trials is an exciting topic for AI researchers. This paper
provides a comprehensive review of research on the intersection of AI and in
silico trials. We summarized the key topics and presented machine learning
formulation and available datasets to consider. Finally, we discussed the
current open challenges of AI and in silico trials that hope to inspire
further research.

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