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Abstract Recently, biotechnology and pharmaceutical industries have made strides to adopt and implement Natural Language Processing (NLP) to address challenges faced when extracting and synthesizing high volumes of information found in unstructured and semistructured text. Here we present, and provide a summary of the findings from, a use case where NLP and text mining methodologies were used to extract clinical trial data from ClinicalTrials.gov for mRNA cancer vaccines.

Abstract The success of antibodies targeting Programmed cell death protein 1 (PD-1) and its ligand L1 (PD-L1) in cancer treatment and the need for improving response rates has led to an increased demand for the development of combination therapies with anti-PD-1/PD-L1 blockers as a backbone. As more and more drugs with translational potential are identified, the number of clinical trials evaluating combinations has increased considerably and the demand to prioritize combinations having potential for success over the ones that are unlikely to be successful is rising. This review aims to address the unmet need to prioritize cancer immunotherapy combinations through comprehensive search of potential drugs and ranking them based on their mechanism of action, clinical efficacy and safety. As lung cancer is one of the most frequently studied cancer types, combinations that showed potential for the treatment of lung cancer were prioritized. A literature search was performed to identify drugs with potential in combination with PD-1/PD-L1 blockers and the drugs were ranked based on their mechanism of action and known clinical efficacy. Nineteen drugs or drug classes were identified from an internal list of lead molecules and were scored for their clinical potential. Efficacy and safety data from pivotal studies was summarized for the selected drugs. Further, overlap of mechanisms of action and adverse events was visualized using a heat map illustration to help screen drugs for combinations. The quantitative scoring methodology provided in this review could serve as a template for preliminary ranking of novel combinations.

Background: Atezolizumab has been studied in multiple indications for both pediatric and adult patient populations. Generally, clinical studies enrolling pediatric patients may not collect sufficient pharmacokinetic data to characterize the drug exposure and disposition because of operational, ethical, and logistical challenges including burden to children and blood sample volume limitations. Therefore, mechanistic modeling and simulation may serve as a tool to predict and understand the drug exposure in pediatric patients.Objective: To use mechanistic physiologically-based pharmacokinetic (PBPK) modeling to predict atezolizumab exposure at a dose of 15 mg/kg (max 1,200 mg) in pediatric patients to support dose rationalization and label recommendations.Methods: A minimal mechanistic PBPK model was used which incorporated age-dependent changes in physiology and biochemistry that are related to atezolizumab disposition such as endogenous IgG concentration and lymph flow. The PBPK model was developed using both in vitro data and clinically observed data in adults and was verified across dose levels obtained from a phase I and multiple phase III studies in both pediatric patients and adults. The verified model was then used to generate PK predictions for pediatric and adult subjects ranging from 2- to 29-year-old.Results: Individualized verification in children and in adults showed that the simulated concentrations of atezolizumab were comparable (76% within two-fold and 90% within three-fold, respectively) to the observed data with no bias for either over- or under-prediction. Applying the verified model, the predicted exposure metrics including Cmin, Cmax, and AUCtau were consistent between pediatric and adult patients with a geometric mean of pediatric exposure metrics between 0.8- to 1.25-fold of the values in adults.Conclusion: The results show that a 15 mg/kg (max 1,200 mg) atezolizumab dose administered intravenously in pediatric patients provides comparable atezolizumab exposure to a dose of 1,200 mg in adults. This suggests that a dose of 15 mg/kg will provide adequate and effective atezolizumab exposure in pediatric patients from 2- to 18-year-old.