Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices.

Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ML-enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human-in-the-loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients.

Tumor growth inhibition modeling in patients with second line biliary tract cancer and first line non-small cell lung cancer based on bintrafusp alfa trials.

This analysis aimed to quantify tumor dynamics in patients receiving either bintrafusp alfa (BA) or pembrolizumab, by population pharmacokinetic (PK)-pharmacodynamic modeling, and investigate clinical and molecular covariates describing the variability in tumor dynamics by pharmacometric and machine-learning (ML) approaches. Data originated from two clinical trials in patients with biliary tract cancer (BTC; NCT03833661) receiving BA and non-small cell lung cancer (NSCLC; NCT03631706) receiving BA or pembrolizumab. Individual drug exposure was estimated from previously developed population PK models. Population tumor dynamics models were developed for each drug-indication combination, and covariate evaluations performed using nonlinear mixed-effects modeling (NLME) and ML (elastic net and random forest models) approaches. The three tumor dynamics' model structures all included linear tumor growth components and exponential tumor shrinkage. The final BTC model included the effect of drug exposure (area under the curve) and several covariates (demographics, disease-related, and genetic mutations). Drug exposure was not significant in either of the NSCLC models, which included two, disease-related, covariates in the BA arm, and none in the pembrolizumab arm. The covariates identified by univariable NLME and ML highly overlapped in BTC but showed less agreement in NSCLC analyses. Hyperprogression could be identified by higher tumor growth and lower tumor kill rates and could not be related to BA exposure. Tumor size over time was quantitatively characterized in two tumor types and under two treatments. Factors potentially related to tumor dynamics were assessed using NLME and ML approaches; however, their net impact on tumor size was considered as not clinically relevant.

Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision Medicine.

Technological innovations, such as artificial intelligence (AI) and machine learning (ML), have the potential to expedite the goal of precision medicine, especially when combined with increased capacity for voluminous data from multiple sources and expanded therapeutic modalities; however, they also present several challenges. In this communication, we first discuss the goals of precision medicine, and contextualize the use of AI in precision medicine by showcasing innovative applications (e.g., prediction of tumor growth and overall survival, biomarker identification using biomedical images, and identification of patient population for clinical practice) which were presented during the February 2023 virtual public workshop entitled "Application of Artificial Intelligence and Machine Learning for Precision Medicine," hosted by the US Food and Drug Administration (FDA) and University of Maryland Center of Excellence in Regulatory Science and Innovation (M-CERSI). Next, we put forward challenges brought about by the multidisciplinary nature of AI, particularly highlighting the need for AI to be trustworthy. To address such challenges, we subsequently note practical approaches, viz., differential privacy, synthetic data generation, and federated learning. The proposed strategies - some of which are highlighted presentations from the workshop - are for the protection of personal information and intellectual property. In addition, methods such as the risk-based management approach and the need for an agile regulatory ecosystem are discussed. Finally, we lay out a call for action that includes sharing of data and algorithms, development of regulatory guidance documents, and pooling of expertise from a broad-spectrum of stakeholders to enhance the application of AI in precision medicine.

Machine Learning in Modeling Disease Trajectory and Treatment Outcomes: An Emerging Enabler for Model-Informed Precision Medicine.

The increasing breadth and depth of resolution in biological and clinical data, including -omics and real-world data, requires advanced analytical techniques like artificial intelligence (AI) and machine learning (ML) to fully appreciate the impact of multi-dimensional population variability in intrinsic and extrinsic factors on disease progression and treatment outcomes. Integration of advanced data analytics in Quantitative Pharmacology is crucial for drug-disease knowledge management, enabling precise, efficient and inclusive drug development and utilization - an application we refer to as model-informed precision medicine. AI/ML enables characterization of the molecular and clinical sources of heterogeneity in disease trajectory, advancing end point qualification and biomarker discovery, and informing patient enrichment for proof-of-concept studies as well as trial designs for efficient evidence generation incorporating digital twins and virtual control arms. Explainable ML methods are valuable in elucidating predictors of efficacy and safety of pharmacological treatments, thereby informing response monitoring and risk mitigation strategies. In oncology, emerging opportunities exist for development of the next generation of disease models via ML-assisted joint longitudinal modeling of high-dimensional biomarker data such as circulating tumor DNA and radiomics profiles as predictors of survival outcomes. Finally, mining real-world data leveraging ML algorithms enables understanding of the impact of exclusion criteria on clinical outcomes, thereby informing rational design of appropriately inclusive clinical trials through data-driven broadening of eligibility criteria. Herein, we provide an overview of the aforementioned contexts of use of ML in drug-disease modeling based on examples across multiple therapeutic areas including neurology, rare diseases, autoimmune diseases, oncology and immuno-oncology.

Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy.

The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model-informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real-world setting. We developed a tumor growth inhibition model based on real-world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image-based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high-dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates.

Semiautomated Pipeline to Quantify Tumor Evolution From Real-World Positron Emission Tomography/Computed Tomography Imaging.

PURPOSE: A semiautomated pipeline for the collection and curation of free-text and imaging real-world data (RWD) was developed to quantify cancer treatment outcomes in large-scale retrospective real-world studies. The objectives of this article are to illustrate the challenges of RWD extraction, to demonstrate approaches for quality assurance, and to showcase the potential of RWD for precision oncology. METHODS: We collected data from patients with advanced melanoma receiving immune checkpoint inhibitors at the Lausanne University Hospital. Cohort selection relied on semantically annotated electronic health records and was validated using process mining. The selected imaging examinations were segmented using an automatic commercial software prototype. A postprocessing algorithm enabled longitudinal lesion identification across imaging time points and consensus malignancy status prediction. Resulting data quality was evaluated against expert-annotated ground-truth and clinical outcomes obtained from radiology reports. RESULTS: The cohort included 108 patients with melanoma and 465 imaging examinations (median, 3; range, 1-15 per patient). Process mining was used to assess clinical data quality and revealed the diversity of care pathways encountered in a real-world setting. Longitudinal postprocessing greatly improved the consistency of image-derived data compared with single time point segmentation results (classification precision increased from 53% to 86%). Image-derived progression-free survival resulting from postprocessing was comparable with the manually curated clinical reference (median survival of 286 v 336 days, P = .89). CONCLUSION: We presented a general pipeline for the collection and curation of text- and image-based RWD, together with specific strategies to improve reliability. We showed that the resulting disease progression measures match reference clinical assessments at the cohort level, indicating that this strategy has the potential to unlock large amounts of actionable retrospective real-world evidence from clinical records.

Predicting disease activity in patients with multiple sclerosis: An explainable machine-learning approach in the Mavenclad trials.

Multiple sclerosis (MS) is among the most common autoimmune disabling neurological conditions of young adults and affects more than 2.3 million people worldwide. Predicting future disease activity in patients with MS based on their pathophysiology and current treatment is pivotal to orientate future treatment. In this respect, we used machine learning to predict disease activity status in patients with MS and identify the most predictive covariates of this activity. The analysis is conducted on a pooled population of 1935 patients enrolled in three cladribine tablets clinical trials with different outcomes: relapsing-remitting MS (from CLARITY and CLARITY-Extension trials) and patients experiencing a first demyelinating event (from the ORACLE-MS trial). We applied gradient-boosting (from XgBoost library) and Shapley Additive Explanations (SHAP) methods to identify patients' covariates that predict disease activity 3 and 6 months before their clinical observation, including patient baseline characteristics, longitudinal magnetic resonance imaging readouts, and neurological and laboratory measures. The most predictive covariates for early identification of disease activity in patients were found to be treatment duration, higher number of new combined unique active lesion count, higher number of new T1 hypointense black holes, and higher age-related MS severity score. The outcome of this analysis improves our understanding of the mechanism of onset of disease activity in patients with MS by allowing their early identification in clinical settings and prompting preventive measures, therapeutic interventions, or more frequent patient monitoring.

Pharmacometric modeling and machine learning analyses of prognostic and predictive factors in the JAVELIN Gastric 100 phase III trial of avelumab.

Avelumab (anti-PD-L1) is an approved anticancer treatment for several indications. The JAVELIN Gastric 100 phase III trial did not meet its primary objective of demonstrating superior overall survival (OS) with avelumab maintenance versus continued chemotherapy in patients with advanced gastric cancer/gastroesophageal junction cancer; however, the OS rate was numerically higher with avelumab at timepoints after 12 months. Machine learning (random forests, SIDEScreen, and variable-importance assessments) was used to build models to identify prognostic/predictive factors associated with long-term OS and tumor growth dynamics (TGDs). Baseline, re-baseline, and longitudinal variables were evaluated as covariates in a parametric time-to-event model for OS and Gompertzian population model for TGD. The final OS model incorporated a treatment effect on the log-logistic shape parameter but did not identify a treatment effect on OS or TGD. Variables identified as prognostic for longer OS included older age; higher gamma-glutamyl transferase (GGT) or albumin; absence of peritoneal carcinomatosis; lower neutrophil-lymphocyte ratio, lactate dehydrogenase, or C-reactive protein (CRP); response to induction chemotherapy; and Eastern Cooperative Oncology Group performance status of 0. Among baseline and time-varying covariates, the largest effects were found for GGT and CRP, respectively. Liver metastasis at re-baseline predicted higher tumor growth. Tumor size after induction chemotherapy was associated with number of metastatic sites and stable disease (vs. response). Asian region did not impact OS or TGD. Overall, an innovative workflow supporting pharmacometric modeling of OS and TGD was established. Consistent with the primary trial analysis, no treatment effect was identified. However, potential prognostic factors were identified.

Model-informed assessment of ethnic sensitivity and dosage justification for Asian populations in the global clinical development and use of cladribine tablets.

Cladribine tablets have been approved in many countries for the treatment of patients with various forms of relapsing multiple sclerosis (MS). Cladribine has a unique pharmacokinetic/pharmacodynamic (PK/PD) profile with a short elimination half-life (~ 1 day) relative to a prolonged PD effect on specific immune cells (most notably a reversible reduction in B and T lymphocyte counts). This results in a short dosing schedule (up to 20 days over 2 years of treatment) to sustain efficacy for at least another 2 years. Global clinical studies were conducted primarily in White patients, in part due to the distinctly higher prevalence of MS in White patients. Given the very low prevalence in Asian countries, MS is considered as a rare disease there. In spite of the limited participation of Asian patients, to demonstrate favorable benefit/risk profile in the treatment of MS demanded application of a Totality of Evidence approach to assess ethnic sensitivity for informing regulatory filings in Asian countries and supporting clinical use of cladribine in Asian patients. Population PD modeling and simulation of treatment-related reduction in absolute lymphocyte count, as a mechanism-related biomarker of drug effect, confirmed consistent PDs in Asian and non-Asian patients with MS, supporting absence of ethnic sensitivity and a common dosage across populations. Through this example, we demonstrate the value of holistic integration of all available data using a model-informed drug development (MIDD) framework and a Totality of Evidence mindset to evaluate ethnic sensitivity in support of Asia-inclusive development and use of the drug across populations.

Relevance of primary lesion location, tumour heterogeneity and genetic mutation demonstrated through tumour growth inhibition and overall survival modelling in metastatic colorectal cancer.

AIMS: The aims of this work were to build a semi-mechanistic tumour growth inhibition (TGI) model for metastatic colorectal cancer (mCRC) patients receiving either cetuximab + chemotherapy or chemotherapy alone and to identify early predictors of overall survival (OS). METHODS: A total of 1716 patients from 4 mCRC clinical studies were included in the analysis. The TGI model was built with 8973 tumour size measurements where the probability of drop-out was also included and modelled as a time-to-event variable using parametric survival models, as it was the case in the OS analysis. The effects of patient- and tumour-related covariates on model parameters were explored. RESULTS: Chemotherapy and cetuximab effects were included in an additive form in the TGI model. Development of resistance was found to be faster for chemotherapy (drug effect halved at wk 8) compared to cetuximab (drug effect halved at wk 12). KRAS wild-type status and presenting a right-sided primary lesion were related to a 3.5-fold increase in cetuximab drug effect and a 4.7× larger cetuximab resistance, respectively. The early appearance of a new lesion (HR = 4.14), a large tumour size at baseline (HR = 1.62) and tumour heterogeneity (HR = 1.36) were the main predictors of OS. CONCLUSIONS: Semi-mechanistic TGI and OS models have been developed in a large population of mCRC patients receiving chemotherapy in combination or not with cetuximab. Tumour-related predictors, including a machine learning derived-index of tumour heterogeneity, were linked to changes in drug effect, resistance to treatment or OS, contributing to the understanding of the variability in clinical response.

Population pharmacokinetic model selection assisted by machine learning.

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary cross-entropy loss. Machine learning provided a selection based only on statistical rules and achieved accurate selection. The minimization process of genetic algorithm was successful at allowing the algorithm to select plausible models. Neural network classification tasks achieved the most accurate results. Neural network regression tasks were less precise than neural network classification and genetic algorithm methods. The computational gain obtained by using machine learning was substantial, especially in the case of neural networks. We demonstrated that machine learning methods can greatly increase the efficiency of pharmacokinetic population model selection in case of large datasets or complex models requiring long run-times. Our results suggest that machine learning approaches can achieve a first fast selection of models which can be followed by more conventional pharmacometric approaches.

Fast screening of covariates in population models empowered by machine learning.

One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervised learning algorithms using importance scores. We compare various classical methods with three machine learning (ML) methods applied to NONMEM empirical Bayes estimates: random forest, neural networks (NNs), and support vector regression (SVR). The performance of the ML models is assessed using receiver operating characteristic (ROC) curves. The F1 score, which measures test accuracy, is used to compare ML and PMX approaches. Methods are applied to different scenarios of covariate influence based on simulated pharmacokinetics data. ML achieved similar or better F1 scores than stepwise covariate modeling (SCM) and conditional sampling for stepwise approach based on correlation tests (COSSAC). Correlations between covariates and the number of false covariates does not affect the performance of any method, but effect size has an impact. Methods are not equivalent with respect to computational speed; SCM is 30 and 100-times slower than NN and SVR, respectively. The results are validated in an additional scenario involving 100 covariates. Taken together, the results indicate that ML methods can greatly increase the efficiency of population covariate model building in the case of large datasets or complex models that require long run-times. This can provide fast initial covariate screening, which can be followed by more conventional PMX approaches to assess the clinical relevance of selected covariates and build the final model.

Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities.

The exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artificial intelligence (AI) and machine learning (ML). Patient-centered iterative forward and reverse translation is at the heart of precision medicine discovery and development across the continuum from target validation to optimization of pharmacotherapy. Integration of advanced analytics into the practice of Translational Medicine is now a fundamental enabler to fully exploit information contained in diverse sources of big data sets such as "omics" data, as illustrated by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome. In this commentary, we provide an overview of ML applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, dose) and offer perspectives on their potential to transform the science and practice of the discipline. Opportunities for integrating ML approaches into the discipline of Pharmacometrics are discussed and will revolutionize the practice of model-informed drug discovery and development. Finally, we posit that joint efforts of Clinical Pharmacology, Bioinformatics, and Biomarker Technology experts are vital in cross-functional team settings to realize the promise of AI/ML-enabled Translational and Precision Medicine.

Population pharmacokinetics of ATR inhibitor berzosertib in phase I studies for different cancer types.

PURPOSE: Berzosertib (formerly M6620) is the first-in-class inhibitor of ataxia-telangiectasia and Rad3-related protein, a key component of the DNA damage response, and being developed in combination with chemotherapy for the treatment of patients with advanced cancers. The objectives of this analysis were to characterize the pharmacokinetics (PK) of berzosertib across multiple studies and parts, estimate inter-individual variability, and identify covariates that could explain such variability. METHODS: A population PK analysis was performed using the combined dataset from two phase I clinical studies (NCT02157792, EudraCT 2013-005100-34) in patients with advanced cancers receiving an intravenous infusion of berzosertib alone or in combination with chemotherapy. The analysis included data from 240 patients across 11 dose levels (18-480 mg/m2). Plasma concentration data were modeled with a non-linear mixed-effect approach and clinical covariates were evaluated. RESULTS: PK data were best described by a two-compartment linear model. For a typical patient, the estimated clearance (CL) and intercompartmental CL were 65 L/h and 295 L/h, respectively, with central and peripheral volumes estimated to be 118 L and 1030 L, respectively. Several intrinsic factors were found to influence berzosertib PK, but none were considered clinically meaningful due to a very limited effect. Model simulations indicated that concentrations of berzosertib exceeded p-Chk1 (proximal pharmacodynamic biomarker) IC50 at recommended phase II doses in combination with carboplatin, cisplatin, and gemcitabine. CONCLUSIONS: There was no evidence of a clinically significant PK interaction between berzosertib and evaluated chemo-combinations. The covariate analysis did not highlight any need for dosing adjustments in the population studied to date. CLINICAL TRIAL INFORMATION: NCT02157792, EudraCT 2013-005100-34.

Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival.

Total tumor size (TS) metrics used in TS models in oncology do not consider tumor heterogeneity, which could help to better predict drug efficacy. We analyzed individual target lesions (iTLs) of patients with metastatic colorectal carcinoma (mCRC) to determine differences in TS dynamics by using the ClassIfication Clustering of Individual Lesions (CICIL) methodology. Results from subgroup analyses comparing genetic mutations and TS metrics were assessed and applied to survival analyses. Data from four mCRC clinical studies were analyzed (1781 patients, 6369 iTLs). CICIL was used to assess differences in lesion TS dynamics within a tissue (intra-class) or across different tissues (inter-class). First, lesions were automatically classified based on their location. Cross-correlation coefficients (CCs) determined if each pair of lesions followed similar or opposite dynamics. Finally, CCs were grouped by using the K-means clustering method. Heterogeneity in tumor dynamics was lower in the intra-class analysis than in the inter-class analysis for patients receiving cetuximab. More tumor heterogeneity was found in KRAS mutated patients compared to KRAS wild-type (KRASwt) patients and when using sum of longest diameters versus sum of products of diameters. Tumor heterogeneity quantified as the median patient's CC was found to be a predictor of overall survival (OS) (HR = 1.44, 95% CI 1.08-1.92), especially in KRASwt patients. Intra- and inter-tumor tissue heterogeneities were assessed with CICIL. Derived metrics of heterogeneity were found to be a predictor of OS time. Considering differences between lesions' TS dynamics could improve oncology models in favor of a better prediction of OS.

The Clinical Pharmacology of Cladribine Tablets for the Treatment of Relapsing Multiple Sclerosis.

Cladribine Tablets (MAVENCLAD®) are used to treat relapsing multiple sclerosis (MS). The recommended dose is 3.5 mg/kg, consisting of 2 annual courses, each comprising 2 treatment weeks 1 month apart. We reviewed the clinical pharmacology of Cladribine Tablets in patients with MS, including pharmacokinetic and pharmacometric data. Cladribine Tablets are rapidly absorbed, with a median time to reach maximum concentration (Tmax) of 0.5 h (range 0.5-1.5 h) in fasted patients. When administered with food, absorption is delayed (median Tmax 1.5 h, range 1-3 h), and maximum concentration (Cmax) is reduced by 29% (based on geometric mean). Area under the concentration-time curve (AUC) is essentially unchanged. Oral bioavailability of cladribine is approximately 40%, pharmacokinetics are linear and time-independent, and volume of distribution is 480-490 L. Plasma protein binding is 20%, independent of cladribine plasma concentration. Cladribine is rapidly distributed to lymphocytes and retained (either as parent drug or its phosphorylated metabolites), resulting in approximately 30- to 40-fold intracellular accumulation versus extracellular concentrations as early as 1 h after cladribine exposure. Cytochrome P450-mediated biotransformation of cladribine is of minor importance. Cladribine elimination is equally dependent on renal and non-renal routes. In vitro studies indicate that cladribine efflux is minimally P-glycoprotein (P-gp)-related, and clinically relevant interactions with P-gp inhibitors are not expected. Cladribine distribution across membranes is primarily facilitated by equilibrative nucleoside transporter (ENT) 1, concentrative nucleoside transporter (CNT) 3 and breast cancer resistance protein (BCRP), and there is no evidence of any cladribine-related effect on heart rate, atrioventricular conduction or cardiac repolarisation (QTc interval prolongation). Cladribine Tablets are associated with targeted lymphocyte reduction and durable efficacy, with the exposure-effect relationship showing the recommended dose is appropriate in reducing relapse risk.

Effects of Postponing Treatment in the Second Year of Cladribine Administration: Clinical Trial Simulation Analysis of Absolute Lymphocyte Counts and Relapse Rate in Patients with Relapsing-Remitting Multiple Sclerosis.

INTRODUCTION: Cladribine Tablets (MAVENCLAD®) selectively reduce absolute lymphocyte counts (ALCs) in patients with multiple sclerosis. The recommended cumulative dose of Cladribine Tablets is 3.5 mg/kg over 4-5 days in months 1 and 2 of treatment years 1 and 2, followed by prolonged efficacy with no additional treatment. After the cladribine-induced reduction, ALCs recover to normal within each treatment year in most patients. Those patients with slow ALC recovery can develop Grade 3-4 lymphopenia, especially those patients with Grade ≥  2 lymphopenia at the start of year 2. Guidelines allowing treatment postponements during year 2 have been proposed for patients with a low ALC, subsequent to CLARITY, the pivotal clinical trial. METHODS: A virtual population was generated using characteristics from CLARITY patients. A clinical trial simulation was performed to determine the impact of alternative treatment scenarios on ALC and relapse rate, by postponing treatment in year 2 to allow for longer ALC recovery time in patients who required it. Should a patient not recover to normal ALC (Grade 0) or Grade 1 lymphopenia within the period defined in the treatment algorithm, treatment in year 2 was suspended. RESULTS: Results were similar across considered scenarios, which implemented different postponement durations. Specifically, ~  92% of virtual subjects did not require treatment postponement and <  1% discontinued due to Grade 2-4 lymphopenia at the end of the maximally permitted postponement. Less severe lymphopenia was observed during year 2 when a treatment algorithm was applied. The effect on relapse rate over 2 years was negligible. CONCLUSIONS: Results support treatment guidelines to decrease the risk of severe lymphopenia following treatment with Cladribine Tablets, while preserving efficacy. TRIAL REGISTRATION: CLARITY; ClinicalTrials.gov: NCT00213135.

Correction to: The Clinical Pharmacology of Cladribine Tablets for the Treatment of Relapsing Multiple Sclerosis.

The cladribine prodrug is phosphorylated intracellularly to its active product, 2-chlorodeoxyadenosine triphosphate (Cd-ATP), by deoxycytidine kinase.