History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications.

Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.

A Quantitative Systems Pharmacology Model of Gaucher Disease Type 1 Provides Mechanistic Insight Into the Response to Substrate Reduction Therapy With Eliglustat.

Gaucher's disease type 1 (GD1) leads to significant morbidity and mortality through clinical manifestations, such as splenomegaly, hematological complications, and bone disease. Two types of therapies are currently approved for GD1: enzyme replacement therapy (ERT), and substrate reduction therapy (SRT). In this study, we have developed a quantitative systems pharmacology (QSP) model, which recapitulates the effects of eliglustat, the only first-line SRT approved for GD1, on treatment-naïve or patients with ERT-stabilized adult GD1. This multiscale model represents the mechanism of action of eliglustat that leads toward reduction of spleen volume. Model capabilities were illustrated through the application of the model to predict ERT and eliglustat responses in virtual populations of adult patients with GD1, representing patients across a spectrum of disease severity as defined by genotype-phenotype relationships. In summary, the QSP model provides a mechanistic computational platform for predicting treatment response via different modalities within the heterogeneous GD1 patient population.

Quantitative Systems Pharmacology Modeling of Acid Sphingomyelinase Deficiency and the Enzyme Replacement Therapy Olipudase Alfa Is an Innovative Tool for Linking Pathophysiology and Pharmacology.

Acid sphingomyelinase deficiency (ASMD) is a rare lysosomal storage disorder with heterogeneous clinical manifestations, including hepatosplenomegaly and infiltrative pulmonary disease, and is associated with significant morbidity and mortality. Olipudase alfa (recombinant human acid sphingomyelinase) is an enzyme replacement therapy under development for the non-neurological manifestations of ASMD. We present a quantitative systems pharmacology (QSP) model supporting the clinical development of olipudase alfa. The model is multiscale and mechanistic, linking the enzymatic deficiency driving the disease to molecular-level, cellular-level, and organ-level effects. Model development was informed by natural history, and preclinical and clinical studies. By considering patient-specific pharmacokinetic (PK) profiles and indicators of disease severity, the model describes pharmacodynamic (PD) and clinical end points for individual patients. The ASMD QSP model provides a platform for quantitatively assessing systemic pharmacological effects in adult and pediatric patients, and explaining variability within and across these patient populations, thereby supporting the extrapolation of treatment response from adults to pediatrics.

Models for Predicting Stage in Head and Neck Squamous Cell Carcinoma Using Proteomic and Transcriptomic Data.

Late diagnosis is one of the reasons that head and neck squamous cell carcinoma (HNSCC) patients experience relative five-year survival rates ranging from 40%-66%. The molecular-level differences between early and advanced stage HNSCC may provide insight into therapeutic targets and strategies. Previous bioinformatics studies have shown mixed or limited results in identifying gene and protein markers and in developing models for discriminating between early and advanced stage HNSCC. Thus, we have investigated models for HNSCC stage prediction using RNAseq and reverse phase protein array data from The Cancer Genome Atlas and The Cancer Proteome Atlas. We systematically assessed individual and ensemble binary classifiers, using filter and wrapper feature selection methods, to develop several well-performing models. In particular, integrated models harnessing both data types consistently resulted in better performance. This study identifies informative protein and gene feature sets which may increase understanding of HNSCC progression.

-Omic and Electronic Health Record Big Data Analytics for Precision Medicine.

OBJECTIVE: Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare. METHODS: In this paper, we present -omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling. RESULTS: To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR. CONCLUSION: Big data analytics is able to address -omic and EHR data challenges for paradigm shift toward precision medicine. SIGNIFICANCE: Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.

DetectTLC: Automated Reaction Mixture Screening Utilizing Quantitative Mass Spectrometry Image Features.

Full characterization of complex reaction mixtures is necessary to understand mechanisms, optimize yields, and elucidate secondary reaction pathways. Molecular-level information for species in such mixtures can be readily obtained by coupling mass spectrometry imaging (MSI) with thin layer chromatography (TLC) separations. User-guided investigation of imaging data for mixture components with known m/z values is generally straightforward; however, spot detection for unknowns is highly tedious, and limits the applicability of MSI in conjunction with TLC. To accelerate imaging data mining, we developed DetectTLC, an approach that automatically identifies m/z values exhibiting TLC spot-like regions in MS molecular images. Furthermore, DetectTLC can also spatially match m/z values for spots acquired during alternating high and low collision-energy scans, pairing product ions with precursors to enhance structural identification. As an example, DetectTLC is applied to the identification and structural confirmation of unknown, yet significant, products of abiotic pyrazinone and aminopyrazine nucleoside analog synthesis. Graphical Abstract ᅟ.

Pan-cancer analysis for studying cancer stage using protein and gene expression data.

Pan-cancer analyses attempt to discover similar features among multiple cancers to identify fundamental patterns common to cancer development and progression. A pan-cancer analysis integrating both protein expression and transcriptomic data is important because it can identify genes that are linked to proteins potentially responsible for a patient's status. This study aims to identify differentially expressed (DE) genes between early and advanced cases of multiple cancer types through the usage of RNA sequencing data. The relevance of these genes is further investigated by developing predictive models using K-nearest neighbor and linear discriminant analysis classifiers. The use of cancer-specific and non-cancer specific features resulted in several moderately performing models. Highlighted genes were further investigated to determine if they encoded for proteins identified in a previously conducted pan-cancer analysis. The results of this study suggest that a pan-cancer analysis may be highly complementary to standard analyses of individual cancers for identifying biologically relevant DE genes and can assist in developing effective predictive models for cancer progression.

A Review of Emerging Technologies for the Management of Diabetes Mellitus.

OBJECTIVE: High prevalence of diabetes mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with clinical decision support systems (CDSSs) facilitating self-disease management and supporting healthcare professionals in decision making. METHODS: A critical literature review analysis is conducted focusing on advances in: 1) sensors for physiological and lifestyle monitoring, 2) models and molecular biomarkers for predicting the onset and assessing the progress of DM, and 3) modeling and control methods for regulating glucose levels. RESULTS: Glucose and lifestyle sensing technologies are continuously evolving with current research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering, and control approaches have been deployed for the development of the CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM. CONCLUSION: Integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized, and participatory diabetes care. SIGNIFICANCE: The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and related challenges are identified.

Developing Robust Predictive Models for Head and Neck Cancer across Microarray and RNA-seq Data.

Increased understanding of the transcriptomic patterns underlying head and neck squamous cell carcinoma (HNSCC) can facilitate earlier diagnosis and better treatment outcomes. Integrating knowledge from multiple studies is necessary to identify fundamental, consistent gene expression signatures that distinguish HNSCC patient samples from disease-free samples, and particularly for detecting HNSCC at an early pathological stage. This study utilizes feature integration and heterogeneous ensemble modeling techniques to develop robust models for predicting HNSCC disease status in both microarray and RNAseq datasets. Several alternative models demonstrated good performance, with MCC and AUC values exceeding 0.8. These models were also applied to discriminate between early pathological stage HNSCC and normal RNA-seq samples, showing encouraging results. The predictive modeling workflow was integrated into a software tool with a graphical user interface. This tool enables HNSCC researchers to harness frequently observed transcriptomic features and ensembles of previously developed models when investigating new HNSCC gene expression datasets.

Pan-cancer analysis for studying cancer stage using protein expression data.

Pan-cancer analyses attempt to discover similar features among multiple cancers in order to identify fundamental patterns common to cancer development and progression. Pan-cancer analysis at the level of protein expression is particularly important because protein expression is more immediately related to patient phenotype than genomic or transcriptomic data. This study aims to analyze differentially expressed (DE) proteins between early and advanced cases of multiple cancer types through the usage of reverse-phase protein array data. The relevance of these proteins is further investigated by developing predictive models using K-nearest neighbor and linear discriminant analysis classifiers. The results of this study suggest that a pan-cancer analysis may be highly complementary to standard analysis of an individual cancer for identifying biologically relevant DE proteins, and can assist in developing effective predictive models for cancer progression.

Comparison of clustering pipelines for the analysis of mass spectrometry imaging data.

Mass spectrometry imaging (MSI) is valuable for biomedical applications because it links molecular and morphological information. However, MSI datasets can be very large, and analyzing them to identify important biological patterns is a challenging computational problem. Many types of unsupervised analysis have been applied to MSI data, and in particular, clustering has recently gained attention for this application. In this paper, we present an exploratory study of the performance of different analysis pipelines using k-means and fuzzy k-means clustering. The results indicate the effects of different pre-processing and parameter selections on identifying biologically relevant patterns in MSI data.

Models for predicting stage in head and neck squamous cell carcinoma using proteomic data.

Head and neck squamous cell carcinoma (HNSCC) that is detected at an advanced stage is associated with much worse patient outcomes than if detected at early stages. This study uses reverse phase protein array (RPPA) data to build predictive models that discriminate between early and advanced stage HNSCC. Individual and ensemble binary classifiers, using filter-based and wrapper-based feature selection, are used to build several models which achieve moderate MCC and AUC values. This study identifies informative protein feature sets which may contribute to an increased understanding of the molecular basis of HNSCC.

Computational nanomedicine: modeling of nanoparticle-mediated hyperthermal cancer therapy.

Nanoparticle-mediated hyperthermia for cancer therapy is a growing area of cancer nanomedicine because of the potential for localized and targeted destruction of cancer cells. Localized hyperthermal effects are dependent on many factors, including nanoparticle size and shape, excitation wavelength and power, and tissue properties. Computational modeling is an important tool for investigating and optimizing these parameters. In this review, we focus on computational modeling of magnetic and gold nanoparticle-mediated hyperthermia, followed by a discussion of new opportunities and challenges.

Mathematical model of the effect of intercellular cooperative interactions in cancer during drug therapy.

We present an agent-based model of head and neck cancer cell population dynamics that investigates the effect of cooperative interactions between individual cancer cells during the course of cytotoxic drug treatment. A model of cooperative behavior based on the Lotka-Volterra competition equations is combined with a model of drug resistance and response. Predictions regarding the individual and combination effects of cooperation and drug treatment qualitatively match experimental observations from the literature.

Multivariate hypergeometric similarity measure.

We propose a similarity measure based on the multivariate hypergeometric distribution for the pairwise comparison of images and data vectors. The formulation and performance of the proposed measure are compared with other similarity measures using synthetic data. A method of piecewise approximation is also implemented to facilitate application of the proposed measure to large samples. Example applications of the proposed similarity measure are presented using mass spectrometry imaging data and gene expression microarray data. Results from synthetic and biological data indicate that the proposed measure is capable of providing meaningful discrimination between samples, and that it can be a useful tool for identifying potentially related samples in large-scale biological data sets.