Lymphocyte responsiveness in rats was found to depend on serum prolactin levels. Blocking pituitary prolactin release with bromocriptine severely reduces lymphocyte reactivity in vitro (mixed lymphocyte reaction) as well as in vivo (graft-versus-host reaction). In addition, evidence for a prolactin/growth hormone-related mRNA species produced in mitogen-and antigen-stimulated lymphocytes has been obtained. Prolactin was shown to compete in a dose-dependent fashion with the immunosuppressant cyclosporine (cyclosporin A) for a common binding site on the surface of T lymphocytes. Further, stimulation of prolactin secretion reversed the immunosuppression induced by cyclosporine. We conclude that prolactin is involved in the maintenance of T-cell immunocompetence and that the immunosuppressive effects of cyclosporine may be mediated by the displacement of prolactin from binding sites on lymphocytes.

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The chapter describes value creation and valuation under structural uncertainty in the healthcare and pharma industries. These risks and uncertainties can significantly influence organizational performance, value creation and long-term sustainability. The discussion continues by comparing traditional valuation concepts used in finance with the requirements posed by the current situation of healthcare business. In particular, patent valuation is a critical business issue, and the value of pharma patents and licensing deals has risen markedly in recent years. Existing evaluation approaches do not consider a patent’s life cycle, an important and unique characteristic of pharma and biotech patents. For this reason, the inherent uncertainty in a patent’s value is modelled as a stochastic process.

This chapter summarizes the overall tendering and contracting process in the pharmaceutical industry by providing an overview of the first-sealed price auction theory, auction rules, and drug pricing mechanism of different countries. Comparing procurement systems across Asia, Africa, Europe, and Latin America, the review casts light on various pharmaceutical bidding systems across the world and their impact on drug prices. Then, this review focuses on the empirical estimation of first-price auction models. In terms of model specification, we compare the two most commonly used empirical methods for bidding price estimation: structural models and reduced form approaches to test the auction theory. Maximum likelihood estimation is the most frequently used method for structural estimation in literature and selection bias correction is widely adopted using reduced form models. In addition to parametric model construction, we also provide an extensive introduction of non-parametric testing methodologies, including non-parametric estimation and quantile-based estimation to reduce the computation complexity and further illustrate how auction theory could be validated by real-world applications. Additional thoughts and adjustments on non-parametric testing are brought up based on a real-world tendering use case from a large multi-national pharmaceutical company.