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3.5 Role ofArticial Intelligence Technology inDrug
Therapy ofEpilepsy
3.5.1 Introduction
Epilepsy is a prevalent chronic neurological disorder worldwide, affecting people of
all ages and ethnic backgrounds. The lifetime risk of epilepsy is approximately 760
per 100,000 individuals. More than 50 million people live with epilepsy, accounting
for 13 million disability-adjusted life years (DALYs) globally [96]. The disease
burden is signicant at both the individual and societal levels. Recurrent seizures
can lead to physical and psychosocial disabilities, while comorbidities such as
learning disabilities and psychiatric disorders complicate epilepsy management
[97]. Premature mortality can be attributed to status epilepticus and Sudden
Unexpected Death in Epilepsy (SUDEP). Epilepsy is associated with social stigma,
unemployment, and marital problems across different cultural backgrounds
[98–100]. Substantial healthcare resources are allocated to epilepsy care, with an
average annual cost of over US$ 1000 per person in high-income countries and an
estimated global healthcare cost of US$ 119 billion per year [101].
Currently, pharmacological therapies remain the primary treatment modality for
epilepsy. ASMs control seizures via various putative mechanisms of action [102],
which include sodium channel blockers (e.g., phenytoin and carbamazepine),
GABA agonists (e.g., benzodiazepines and barbiturates), calcium channel blockers
(e.g., ethosuximide), as well as agents targeting molecular substrates such as synaptic vesicle protein 2A (SV2A) receptors (e.g., levetiracetam and brivaracetam) and
AMPA receptors (e.g., perampanel). The number of ASMs available has signicantly increased in the past two decades, with more than 20in the market currently
[103]. Newer ASMs generally exhibit improved adverse event proles and fewer
drug interactions. However, the effectiveness of these new ASMs in seizure control
does not surpass their older counterparts. Recent studies have shown that the proportion of people with drug-resistant epilepsy (DRE) remains steady at around 30%
of all people living with epilepsy, despite the expanded pharmacological options
[104]. This implies the ASMs are not effective in reversing the course of the disease.
Currently, the pharmacological management of epilepsy is largely empirical.
Once the diagnosis of epilepsy is conrmed, a patient is typically prescribed an
ASM.The choice of drug depends on the clinician’s discretion, considering factors
such as epilepsy classication, patient demographics (e.g., childbearing potential),
comorbidities, and potential drug interactions. The decision-making process may
vary depending on the individual clinician’s experience. If the initial ASM fails to
control seizures, a second ASM may be added to, or substitute for the rst. According
to the criteria of International League Against Epilepsy (ILAE), a diagnosis of DRE
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can be made if a patient do not achieve sustained (12months or more) seizure freedom after adequate trials of two appropriately ASMs [105]. Currently, there are no
reliable biomarkers available to predict DRE at the time of epilepsy diagnosis.
Patients often undergo a trial-and-error process until the right ASM may be selected,
which can take months or even years to achieve the goal of seizure freedom or conrm the diagnosis of DRE [106].
On the other hand, the concept of Personalized Medicine or Precision Medicine
has gained signicant attention in recent years [107]. In the conventional treatment
paradigm, various guidelines on selection of ASMs are based on clinical trials conducted with stringently selected patient groups. These studies draw conclusions by
using large patient cohorts to ensure sufcient statistical power and minimize confounding factors. However, this evidence-based practice has been criticized as a
“one-size-ts-all” approach that neglects the specic characteristics of individuals.
In contrast, Personalized Medicine aims to customize therapy on the individual
level. By gathering personal proles, including demographics, genomics, radiographic, and physiological features, the most suitable therapy can be tailored to each
patient. In the eld of epilepsy, Personalized Medicine promises to be an achievable
goal with advances in technology [108, 109]. For example, the development of
pharmacogenomic panels may allow the prediction of responsiveness and potential
adverse effects of ASMs in an individual patient [110]. Neuroimaging processing
can detect subtle structural substrates of epileptogenicity, thereby enhancing the
success rate of surgical treatment [111]. Automated analysis of EEG signals can be
used to detect seizures and predict the prognosis of epilepsy [112]. All these techniques involve a huge amount of data that overwhelms manual analytical capacity.
3.5.2 Articial Intelligence andMachine Learning
Articial intelligence (AI) refers to the capability of a machine to imitate intelligent
human thinking and learning. AI has a wide application in medicine. It has the
advantage of precision, efciency, and perpetuity. Hence, investment in AI in health
care industry may save cost by reducing medical errors, streamline workow and
reducing manpower [113]. Novel applications emerge continuously; some notable
examples are given below.
Robotics powered by AI has revolutionized the surgical eld. Robotic-assisted
surgery refers to a specialized robotic system to assist or replace the surgeon in
certain procedures to enhance precision and minimize complication risk. These
robotic systems even allow telesurgery, in which surgeons can operate on patients
remotely [114, 115].
In the journey of management, a patient may have multiple consultations with
various specialists or receive treatment from different institutes. Reconstruction of
a patient’s natural history from electronic medical records from different sources is
now possible with Natural Language Processing (NLP) [116]. NLP is a subeld of
AI that focuses on how computers could understand and generate human language.
3 The Basic Principles andPrecautions ofDrug Therapy
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An automatic medical image reporting system involves both computer vision
and NLP.Computer vision algorithms process and analyze medical images. These
algorithms employ various techniques such as image segmentation, object detection, and image classication to identify anatomical structures and abnormalities in
the images. The ndings of the input image are expressed as a structured and coherent textual report with the help of NLP.
The concept of applying AI in decision-making or problem-solving processes can
be traced back to the mid-twentieth century. Clinical Decision Support Systems
(CDSS) are software systems designed to facilitate daily clinical decisions [117].
With the incorporation of AI, the capabilities of CDSS can be enhanced. An exemplary example is the MYCIN system developed in the 1970s, which aimed to facilitate the management of bacterial infections [118]. It consisted of three major
components: the knowledge base, inference engine, and user interface. The knowledge base contained facts provided by human experts, while the user interface facilitated communication between the computer software and the user. The data input into
the inference engine by the user was then interpreted using a preset logic ow. The
MYCIN system employed a backward chaining technique, which is a top-to- bottom
approach that tracks backward to prove the truth of facts. Other examples of CDSS
include an alert warning system in the computerized prescription program, where a
warning signal appears if a patient has a potential drug allergy to the prescribed medication. Over the past two decades, there have been spectacular advances in computer
technology, including the emergence of high-performance processors, massive storage, the internet, and cloud computing, nurturing the next generation of AI.
Machine Learning (ML) is a branch of AI that may help to solve many practical
problems. ML can be broadly divided into supervised learning and unsupervised
learning [119]. In supervised learning, a human-labeled training dataset is used to
train the ML algorithm [120]. The training dataset consists of labeled input features
matched with corresponding categorized outputs. Examples of supervised learning
models include regression, random forest (RF), support vector machine (SVM),
k-nearest neighbor (kNN), and Gradient Boosting Tree (GBT) [121–123]. Supervised
learning has wide-ranging applications, such as classication, NLP, and image recognition. Classication predicts categorical outputs; NLP is used for analyzing text
data (e.g., automated translators); and image recognition is used for recognizing
images (e.g., face recognition systems). Unsupervised learning is usually used in
hidden pattern recognition and association. Examples of unsupervised learning
techniques include K-Means Clustering, Principal Component Analysis (PCA), and
Autoencoders. Deep learning is a more advanced type of ML.It does not require
manual input in feature selection; rather, the model could extract features from raw
data. The algorithm structure is in the form of a multilayered articial neural network. The inputs go through articial neuron layers in a non-linear way and map
into outputs. The deep learning model can also improve its performance as the size
of the training data grows.
To develop an ML model, several key steps are involved, including data preprocessing, algorithm selection, model training, and performance evaluation. In the
data preprocessing stage, data is collected from various sources, and any missing or
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irrelevant data is removed. Sometimes, features or variables in a dataset are transformed, such as conversion from a continuous scale to a categorical value, for the
convenience of model tting.
Next, suitable algorithms are selected based on the specic problem to be solved.
In some cases, multiple models are developed and compared to select the one with
the best performance.
Once the algorithm is selected, the dataset is divided into a training set and a validation set. The training set is used to teach the supervised learning model to make
meaningful predictions. After training is completed, the model will then be validated to assess its performance and generalizability. Various validation techniques
can be employed, such as k-fold cross-validation and leave-one-out cross-validation
(LOOCV). In k-fold cross-validation, the whole dataset is divided into equal portions or folds of number k. The model is then trained on k-1 folds, and one-fold is
left out for validation. The model is trained for k times, so that each of the k folds is
used for validation [124]. In LOOCV, each observation is selected as a validation set
while the rest of the observations (N-1) are considered as the training set. The choice
of validation approach depends on the size of the dataset and the complexity of the
computation involved.
Several metrics are commonly used to assess the performance of ML models.
For example, in a classication task, performance metrics usually include accuracy,
precision, recall, F-score, positive predictive value (PPV), negative predictive value
(NPV), and area under the receiver operating characteristic curve (AUC). The AUC
varies from zero to one, where one signies a perfect classier and 0.5 suggests a
random classier that is not better than chance.
It is valuable to understand the relative contribution of each feature in predicting
within an ML model. This provides insights into the relationships between features
and outcomes and helps improve the model. Established methods, such as model
coefcient and permutation importance, can be used to assess the relative importance of different features in a predictive model. Model coefcient reects the
weight of a feature in a linear model, with the higher coefcient value indicating
greater importance. Permutation importance is a model-agnostic approach that measures the impact of shufing the value of a feature on the model’s performance. By
evaluating the drop in performance after shufing each feature, one can determine
its relative importance.
ML may be applied to solve problems in the development and clinical use of
ASMs. In this chapter, we will focus on two main clinical applications: ASM
response and DRE prediction.
3.5.3 Prediction ofASM Response
Devinsky etal. developed an RF model to predict the ASM with the highest likelihood of seizure control for individual patients utilizing claims data [125]. About
6years of medical claims data were retrieved from a nationwide comprehensive
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claim database in the US.Adults with epilepsy were identied based on International
Classication of Diseases, Ninth Revision (ICD-9) coding and pharmacy claims for
ASMs. The researchers recruited cases with any one of the three conditions: (1)
initiation of rst ever ASM; (2) addition of ASM to an existing regimen; (3) substitution of an ASM by another ASM.The date of these major changes in regimen was
dened as the index date. Patient’s features and the treatment outcomes were
extracted from 12months before and after the index date, respectively. ASM substitution or addition of ASM to an existing regimen or complete withdrawal of the
ASM was presumed to be treatment failure due to either inefcacy or
intolerability.
A huge number of features, about 5000, covering demographics, comorbidities,
therapeutic mechanism of ASMs, were available in each case. To avoid overtting,
a selection process with statistical criteria was implemented to lter out irrelevant
features. The nal training dataset included 34,990 patients, and the validation dataset comprised 8292 patients.
The trained RF model selected the ASM with the highest probability of a successful outcome as output. The machine-selected ASM was compared to the ASM
chosen by physicians. The RF model achieved an AUC of 0.72. The ASM selected
by the model matched with the physician’s choice in only 13% of the cases. In this
group, the treatment outcomes were found to be better with a lower chance of altering the ASM regimen. Other secondary outcomes, such as health service utilization,
also favored ASM selection by the ML model.
This study used a claim database as the data source for developing an ML model.
This kind of administrative database has certain advantages. It contains a large number of cases and is readily available. If the database has a broad coverage, the cohort
obtained could minimize selection bias.
However, the claim database also has its limitations. Clinical details could not be
directly retrieved. Clinical conditions have to be presumed by surrogate markers. In
Devinsky’s study, any change in ASM was supposed to be treatment failure, but it
could not distinguish whether it was due to lack of efcacy or medication intolerance. Moreover, the database could not differentiate whether the ASMs are prescribed for seizure prophylaxis or other purposes, for instance, valproate could be
used as a mood stabilizer and prophylaxis of migraine; gabapentin and pregabalin
are commonly used to treat neuropathic pain. The diagnosis dened by coding may
be subject to the pitfalls of either over-coding or under-coding. Commonly used
clinical outcomes, such as seizure freedom or 50% seizure reduction, could not be
obtained directly from the claim database.
Ouyang et al. designed an SVM model to identify biomarkers to predict the
therapeutic outcomes of ASMs [126]. Quantitative EEG (QEEG) analysis of
patients’ background EEG was utilized as a biomarker to assess therapeutic efcacy. A total of 20 children living with epilepsy, 11 of whom responded to ASMs
and 9 had DRE, were enrolled. EEG recordings were obtained from each patient at
different time points before and after initiation or change of the ASM regimen. The
EEG recordings were then analyzed by the QEEG technique. Discriminative features of the EEG were selected and fed into the SVM model to classify the treatment
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outcomes. A ten-fold cross-validation approach was adopted for internal validation.
Finally, six EEG feature descriptors were found crucial in differentiating treatmenteffective (seizure reduction >50%) and ineffective groups. The performance metric
of the model yielded an accuracy of 83%.
This study integrated clinical and electrophysiological features to enhance the
predictive capabilities of the ML model. However, there are drawbacks in the study
that undermine its generalizability. First, the number of patients was small. Also, the
recruited patients had different seizure subtypes, including both focal and generalized seizures. The EEG features of these seizure subtypes are known to have great
discrepancies. On the other hand, the ASMs used by the patients could also affect
the EEG tracing. In this study, the ineffective group more commonly had patients
using ASM combinations, while the effective group more commonly used monotherapy. The effects of multiple ASMs or their drug interactions may cause bias in
QEEG analysis when compared to a single ASM.Further study with a larger and
more homogeneous patient population is needed to verify the results.
Zhang etal. attempted to estimate the therapeutic outcomes of specic ASMs by
using ML [127]. An SVM classier was developed to predict seizure freedom in
epilepsy patients receiving levetiracetam based on both clinical and QEEG features.
The study recruited 46 patients receiving levetiracetam from a single center in
China. Twenty-two patients achieved seizure freedom while 24 did not. Eleven clinical features were selected as input, including (1) age; (2) duration of epilepsy; (3)
family history of epilepsy; (4) seizure subtype (generalized, focal, or unknown
onset); (5) seizure frequency in the 12months before levetiracetam; 6) psychiatric
comorbidities; (7) seizure circadian rhythm; (8) temporal lobe epilepsy; (9) time
between levetiracetam initiation to the last seizure before levetiracetam; (10) interictal spikes in EEG; (11) positive MRI ndings. For the EEG features, Sample
Entropy of alpha, beta, theta, and delta frequencies was used to describe the data
[128]. Sample entropy is a measurement used in signal processing and time series
analysis to quantify the regularity or predictability of a signal. Four frequency bands
of alpha, beta, theta, and delta were reconstructed and then used to calculate Sample
Entropy for each patient in every channel.
To prevent overtting, only strictly selective bands were extracted and incorporated into the input dataset. Consequently, four bands from four different channels
were selected: beta bands from EEG lead Fp2, alpha bands from F4, theta bands
from C3, and beta bands from F8. The 11 clinical and 4 QEEG features (Sample
Entropy of alpha, beta, theta, and delta) of all patients formed the dataset and were
then used to train and validate the SVM model. The algorithm was validated by vefold cross-validation, hold-out validation, and jack-knife validation. Mean Impact
Value (MIV) then assessed to determine the contribution of each feature to the
model [129]. The results showed that the beta band from Fp2 played a crucial role
in the classier model. Clinical features of temporal lobe epilepsy, family history,
circadian rhythm of seizure, MRI ndings, comorbidity, time between levetiracetam
initiation and last seizure, and duration of epilepsy had descending importance. As
for QEEG features, beta band from Fp2 has a signicant contribution. Interestingly,
3 The Basic Principles andPrecautions ofDrug Therapy
357
age, seizure type, interictal spikes, and seizure frequency before levetiracetam did
not have a notable impact on the model.
The combined model of both clinical and EEG features yielded an AUC of
0.95 in the training dataset and an AUC of 0.96 in the validation dataset. The
researchers tried to compare the performance of the SVM model with input of clinical features only, EEG features only, and combination of clinical and EEG features.
The model with combined input demonstrated the best results.
The superior performance of the combined input integrating clinical and EEG
inputs made clinical sense, as multiple modalities probably enhanced the accuracy
of the classier’s prediction. However, the small number of recruited patients is a
main drawback of the study. The specic bands of the four EEG channels showed a
signicant difference between seizure-free and non-seizure-free groups, but the
authors did not postulate the electrophysiological mechanisms behind their ndings. The high AUC values in both training and validation may imply the model has
high generalizability. It is worthwhile to further evaluate the model in external validation by using an independent cohort.
Yao etal. constructed ML models to predict the outcomes of ASM in new-onset
epilepsy [130]. A total of 287 patients with newly diagnosed epilepsy were
recruited from a single center. The cohort had at least three years of follow-up.
Fourteen demographic and clinical features were selected as input features of the
model, including: (1) sex; (2) living area; (3) occupation; (4) education level; (5)
age at seizure onset; (6) risk factors of epilepsy (including brain trauma, stroke
intracranial infection, perinatal hypoxia, brain tumor); (7) family history of epilepsy; (8) seizure types (focal, generalized, or unknown onset); (9) number of
seizures before treatment; (10) history of febrile seizure; (11) duration of previous
treatment; (12) presence of multiple seizure types (more than one seizure type
including focal aware, focal impaired awareness, focal to bilateral tonic–clonic,
generalized onset, and unknown onset seizures); (13) EEG ndings; (14) MRI
ndings.
In their treatment protocol, monotherapy of ASM was selected by the attending
epileptologist. One of seven ASMs were selected: carbamazepine, oxcarbazepine,
valproate, lamotrigine, levetiracetam, topiramate, and gabapentin. Outcomes from
the rst ASM were classied into three groups: early remission (less than six months
after ASM commencement), late remission (more than six months after ASM commencement), and no remission (ongoing seizures after one year of ASM treatment).
In the dataset, there were 207 remission cases, of which 140 were early remission
and 67 were late remission, along with 80 cases of no remission.
The researchers compared the performance of ve supervised ML models:
Decision Tree, RF, SVM, Extreme Gradient Boosting (XGBoost), and Logistic
Regression. Two experiments were conducted. In the rst experiment, all 287
patients’ data were used as a training dataset, aiming to predict remission versus no
remission status. In the second experiment, data from 207 patients in the remission
group were used as a training dataset, aiming to predict early versus late remission.
A ve-fold cross-validation was carried out for internal validation.
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Q. Wang et al.
Among the ve models, the XGBoost algorithm achieved the best metrics in
predicting remission versus no remission, with an F1 score of 0.95 and an AUC of
0.98. The best predictor for remission versus no remission was the number of seizures before treatment. The XGBoost model also performed the best in predicting
early remission versus later remission, with an F1 score of 0.84 and an AUC of 0.92.
The presence of multiple seizure types had the most signicant impact in predicting
early versus late remission.
This study compares different ML models instead of preselection of a single
model, therefore enhancing transparency in model selection. Another notable point
is that the input features cover investigational results in addition to other clinical
features from medical history and demographics. Both the EEG and MRI ndings
are incorporated as categorical features in the dataset. The XGBoost model achieved
impressively high F1 and AUC values in predicting early remission versus late
remission and remission versus no remission. One of the contributing factors was
the application of grid search to optimize the hyperparameter set in hyperparameter
tuning. Hyperparameters are parameters that are not learned during the training
process, but are set before training and determine how the model learns and generalizes from the data. Hyperparameter optimization is the process of nding the best
set of hyperparameters for an ML model. Of course, grid search also has its drawbacks, such as the requirement of additional computational resources and inability
to deal with a large number of hyperparameters or extensive ranges of hyperparameter values.
Two related studies, conducted by Petrovski etal. and Shazadi etal., aimed to
predict response to ASM in newly diagnosed epilepsy patients by exploiting pharmacogenomic markers [131, 132].
Petrovski etal. developed a multi-single nucleotide polymorphisms (SNPs) classication model to predict the treatment outcome of the rst ASM in patients with
newly diagnosed epilepsy [131]. A total of 115 patients with newly diagnosed epilepsy from two hospitals in Australia were enrolled. Treatment response was
assessed at the end of the follow-up period of one year. Drug responders were
dened as seizure freedom with the rst ASM, while non-responders were dened
as those with recurrent seizures while on the initial ASM.
Genotyping for 4041 SNPs in 279 candidate genes was performed. These candidate genes were chosen based on their roles in the pathogenesis of epilepsy or drug
metabolism, as well as their high expression levels in the brain. The list of SNPs was
further narrowed down, and ve sophistically selected SNPs were nally included
in the ML model development. The algorithm of kNN was employed as a classier.
A ve-fold cross-validation approach was employed for internal validation. In
addition, the model was further externally validated by two independent validation
cohorts. The rst validation cohort comprised 63 newly diagnosed epilepsy patients
from the same population, and the second cohort comprised 108 community-treated
chronic epilepsy patients. The performance of the multi-SNP model was further
compared with models based on individual SNAPs alone in each validation cohort.
The performance metrics showed promising results. In the cross-validation, the
model achieved an accuracy of 84%. In the validation using the 63 newly diagnosed
3 The Basic Principles andPrecautions ofDrug Therapy
359
epilepsy patients, the model yielded a sensitivity of 91% and specicity of 53%. For
the chronically treated epilepsy validation cohort, the multi-SNP classier model
achieved a sensitivity of 81% and specicity of 50%. The multi-SNP models consistently outperformed the single-SNP models in both independent validation
cohorts.
Shazadi etal. further investigated Petrovski’s multi-SNP model by externally
validating it with larger independent cohorts [132]. They utilized two cohorts from
the UK: the Glasgow cohort, consisting of 281 new-onset epilepsy patients, and a
subset of patients from the SANAD study, which included 491 patients with genetic
sampling [133]. The genotyping process was the same as in Petrovski’s study,
focusing on the ve specic SNPs. The kNN algorithm retained identical input features as the original model. The UK cohorts were used to validate the multi-SNP
model in three ways: (1) Petrovski’s training dataset served as the training dataset,
and each of the two UK cohorts as the test datasets; (2) retraining the kNN model
using the SANAD cohort as both training and test datasets. A random 70:30 traintest split was adopted. The parameters of kNN were identical to those employed in
the original Petrovski’s model; (3) testing the performance of Petrovski’s kNN
model using an LOOCV approach in the UK cohorts.
The results showed that the multi-SNAPs model could reasonably predict the
treatment responses in carbamazepine or valproate in both Glasgow and SANAD
cohorts (Glasgow: odds ratio [OR]=3.1, 95% condence interval [CI]=1.4–6.6,
p=0.018; SANAD: OR=2.8, 95% CI: 1.3–6.1, p=0.048). However, the model
could not reliably predict lamotrigine and other ASM treatment responses in both
UK cohorts.
These two studies tried to predict the response to ASM using genetic approach.
The hypothesis that multiple SNPs could have a better prediction than a single SNP
is sensible, as response to ASM is probably a trait inuenced by multiple genetic
variants. The possible reason for the failed external validation of the ML model on
the two UK cohorts may be because of different prescription patterns between the
Australian and UK cohorts. Carbamazepine and valproate were more commonly
used for the new-onset epilepsy in the Australian cohorts, while lamotrigine or other
ASMs were used more commonly in the UK cohorts [132]. This highlights the preference for prescription in different institutions or countries could be a source of bias
in the dataset. It would be optimal to collect the developmental dataset from a
diverse range of institutions and countries.
De Jong etal. combined both clinical and genetic data to develop an ML to predict the response to brivaracetam [134]. The data sources consisted of two patient
cohorts from two randomized placebo-controlled clinical trials of adjunctive brivaracetam. The rst cohort consisted of 235 patients, while the second cohort consisted
of the other 47 patients. The data from the larger cohort was used for the developmental dataset for both training and internal validation, while the smaller group
served as the retrospective validation dataset. The treatment response was dened as
a>50% reduction in seizure frequency after 12weeks. Both the clinical and genetic
features were incorporated into the ML models. Whole-genome sequencing (WGS)
data of the patient cohorts were available. The huge amount of genetic data from
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Q. Wang et al.
WGS was robustly narrowed down to a workable list of genetic features that related
to mechanisms of epilepsy and drug action. Finally, four data modalities were
employed, including: (1) mutational load scores; (2) polygenic risk score; (3) SV2A
structural variance (the molecular target of brivaracetam); and (4) clinical data.
As some of the input features may be associated with placebo response, the
researchers further validated the models with an extra placebo dataset. A cohort of
235 patients who were assigned to the placebo arm in the same clinical trial was
used as the placebo dataset. However, genetic data was not available in this placebo
cohort. The researchers assessed the placebo response in the model trained only on
clinical data instead.
The researchers compared ve different ML models, including sparse multiblock partial least squares discriminant analysis, multimodal neural network, elastic
net classier, GBT, and logistic regression-based stacking. The ML models underwent internal cross-validation and were further validated externally using the validation dataset.
Among the tested models, the GBT achieved the best performance parameters,
with an AUC of 0.76 (95% CI: 0.76±0.014) in the training dataset and 0.75 (95%
CI: 0.75±0.15) in the validation dataset. Analysis of the placebo cohort ensured
that the model trained on the clinical data modality was not associated with a placebo effect.
The researchers demonstrated that the integration of genetic and clinical data
modalities improved the model’s performance (AUC of 0.76) compared to using
clinical data alone (AUC of 0.71). Genetic factors included the presence of structural variants overlapping the SV2A gene and the mutational load in a gene set
representing microtubule minus end binding. The only clinical factor that predicted
brivaracetam responsiveness was prior levetiracetam use. Failure in levetiracetam
use predicted a poor brivaracetam response.
This study has demonstrated that a meaningful model could be developed with a
relatively small cohort. Such a drug response prediction model focusing on a single
ASM could facilitate enrichment trials. Eligible subjects with a high probability of
non-responsiveness could be screened out using an ML model before being recruited
into a drug trial. This measure could effectively reduce the sample size by increasing the responder rate, making it easier to achieve statistically signicant differences between the treatment and placebo groups in randomized controlled trials. It
would also shorten the drug trial process and reduce the cost of drug development.
However, the short duration of follow-up (12weeks) and small absolute difference
in seizure frequency in the corresponding clinical trials make the reliability of the
developmental dataset questionable. On the other hand, the contribution of genetic
data to the nal model was surprisingly small, as reected by the small increase
from AUC 0.71 of the model with clinical features alone to AUC 0.76 of the model
with a combination of clinical plus genetic features [135]. The cost-effectiveness of
incorporating genetic data into the ML model is challenged.
Hakeem etal. have developed and validated a deep learning model to predict the
response of the rst ASM for new-onset epilepsy [136]. Five cohorts from Scotland,