Two heads are better than one: current landscape of integrating QSP and machine learning

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP ? ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices

Use of Quantitative Pharmacology in the Development of HAE1, a High-Affinity Anti-IgE Monoclonal Antibody

HAE1, a high-affinity anti-IgE monoclonal antibody, is discussed here as a case study in the use of quantitative pharmacology in the development of a second-generation molecule. In vitro, preclinical, and clinical data from the first-generation molecule, omalizumab, were heavily leveraged in the HAE1 program. A preliminary mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model for HAE1 was developed using an existing model for omalizumab, together with in vitro binding data for HAE1 and omalizumab. When phase I data were available, the model was refined by simultaneously modeling PK/PD data from omalizumab studies with the available HAE1 phase I data. The HAE1 clinical program was based on knowledge of the quantitative relationship between a pharmacodynamic biomarker, suppression of free IgE, and clinical response (e.g., lower exacerbation rates) obtained in pivotal studies with omalizumab. A clinical trial simulation platform was developed to predict free IgE levels and clinical responses following attainment of a target free IgE level (≤10 IU/ml). The simulation platform enabled selection of four doses for the phase II dose-ranging trial by two independent methods: dose-response non-linear fitting and linear mixed modeling. Agreement between the two methods provided confidence in the doses selected. Modeling and simulation played a large role in supporting acceleration of the HAE1 program by enabling data-driven decision-making, often based on confirmation of projections and/or learning from incoming new data

Linked Pharmacometric-Pharmacoeconomic Modeling and Simulation in Clinical Drug Development

Market access and pricing of pharmaceuticals are increasingly contingent on the ability to demonstrate comparative effectiveness and cost-effectiveness. As such, it is widely recognized that predictions of the economic potential of drug candidates in development could inform decisions across the product life cycle. This may be challenging when safety and efficacy profiles in terms of the relevant clinical outcomes are unknown or highly uncertain early in product development. Linking pharmacometrics and pharmacoeconomics, such that outputs from pharmacometric models serve as inputs to pharmacoeconomic models, may provide a framework for extrapolating from early-phase studies to predict economic outcomes and characterize decision uncertainty. This article reviews the published studies that have implemented this methodology and used simulation to inform drug development decisions and/or to optimize the use of drug treatments. Some of the key practical issues involved in linking pharmacometrics and pharmacoeconomics, including the choice of final outcome measures, methods of incorporating evidence on comparator treatments, approaches to handling multiple intermediate end points, approaches to quantifying uncertainty, and issues of model validation are also discussed. Finally, we have considered the potential barriers that may have limited the adoption of this methodology and suggest that closer alignment between the disciplines of clinical pharmacology, pharmacometrics, and pharmacoeconomics, may help to realize the potential benefits associated with linked pharmacometric-pharmacoeconomic modeling and simulation

Reduction and Return of Infectious Trachoma in Severely Affected Communities in Ethiopia

Trachoma is one of the leading causes of blindness in the developing world. The World Health Organization has a multi-pronged approach to controlling the ocular chlamydial infection that causes the disease, including distributing antibiotics to entire communities. Even a single community treatment dramatically reduces the prevalence of the infection. Unfortunately, infection returns back into communities after treatment, at least in severely affected areas such as rural Ethiopia. Here, we assess whether additional scheduled treatments in 16 communities in the Gurage area of Ethiopia further reduce infection, and whether the disease returns after distributions are stopped. In communities with the highest levels of trachoma ever studied, we find that repeated mass oral azithromycin distributions gradually reduce the prevalence of trachoma infection in a community, as long as these treatments are given frequently enough and to enough people in the community. Unfortunately, infection returns into the communities after the last treatment. Sustainable changes or complete local elimination of infection will be necessary to stop the return of ocular chlamydial in communities with very high prevalence of the disease

New insights into the synergism of nucleoside analogs with radiotherapy

Nucleoside analogs have been frequently used in combination with radiotherapy in the clinical setting, as it has long been understood that inhibition of DNA repair pathways is an important means by which many nucleoside analogs synergize. Recent advances in our understanding of the structure and function of deoxycytidine kinase (dCK), a critical enzyme required for the anti-tumor activity for many nucleoside analogs, have clarified the mechanistic role this kinase plays in chemo- and radio-sensitization. A heretofore unrecognized role of dCK in the DNA damage response and cell cycle machinery has helped explain the synergistic effect of these agents with radiotherapy. Since most currently employed nucleoside analogs are primarily activated by dCK, these findings lend fresh impetus to efforts focused on profiling and modulating dCK expression and activity in tumors. In this review we will briefly review the pharmacology and biochemistry of the major nucleoside analogs in clinical use that are activated by dCK. This will be followed by discussions of recent advances in our understanding of dCK activation via post-translational modifications in response to radiation and current strategies aimed at enhancing this activity in cancer cells

In Vivo Methods to Study Uptake of Nanoparticles into the Brain

Several in vivo techniques have been developed to study and measure the uptake of CNS compounds into the brain. With these techniques, various parameters can be determined after drug administration, including the blood-to-brain influx constant (Kin), the permeability-surface area (PS) product, and the brain uptake index (BUI). These techniques have been mostly used for drugs that are expected to enter the brain via transmembrane diffusion or by carrier-mediated transcytosis. Drugs that have limitations in entering the brain via such pathways have been encapsulated in nanoparticles (based on lipids or synthetic polymers) to enhance brain uptake. Nanoparticles are different from CNS compounds in size, composition and uptake mechanisms. This has led to different methods and approaches to study brain uptake in vivo. Here we discuss the techniques generally used to measure nanoparticle uptake in addition to the techniques used for CNS compounds. Techniques include visualization methods, behavioral tests, and quantitative methods

A Practitioner’s Guide to Performing a Holistic Evaluation of Technology-Enhanced Learning in Medical Education

Technology-enhanced learning (TEL) is now a common mode of educational delivery within medical education. Despite this upsurge, there remains a paucity in comprehensive evaluation of TEL efficacy. In order to make meaningful and evidence-informed decisions on ‘how’ and ‘when’ to utilise technology within a course, ‘useful knowledge’ is required to support faculty in these decision-making processes. In this monograph, a series of pragmatic and achievable approaches for conducting a holistic evaluation of a TEL resource intervention are detailed. These suggestions are based on an established TEL evaluation framework, as well as the author’s own experience and that of the broader literature. The approaches cover development of an appropriate research question that is based on the availability of existing TEL resources alongside the peer-reviewed literature; the development of an appropriate team as well as recommendations for navigating ethical approval; conducting small-scale quantitative and qualitative measure; and performing a large-scale mixed methods assessment to understand the holistic impact of the TEL resource