Can complexity be commercialized?

Applying quantitative and systems pharmacology to drug development and beyond: An introduction to clinical pharmacologists

ABSTRACT

Quantitative and systems pharmacology (QSP) is an innovative and integrative approach combining physiology and pharmacology to accelerate medical research. This review focuses on QSP's pivotal role in drug development and its broader applications, introducing clinical pharmacologists/researchers to QSP's quantitative approach and the potential to enhance their practice and decision-making. The history of QSP adoption reveals its impact in diverse areas, including glucose regulation, oncology, autoimmune disease, and HIV treatment. By considering receptor-ligand interactions of various cell types, metabolic pathways, signaling networks, and disease biomarkers simultaneously, QSP provides a holistic understanding of interactions between the human body, diseases, and drugs. Integrating knowledge across multiple time and space scales enhances versatility, enabling insights into personalized responses and general trends. QSP consolidates vast data into robust mathematical models, predicting clinical trial outcomes and optimizing dosing based on preclinical data. QSP operates under a "learn and confirm paradigm," integrating experimental findings to generate testable hypotheses and refine them through precise experimental designs. An interdisciplinary collaboration involving expertise in pharmacology, biochemistry, genetics, mathematics, and medicine is vital. QSP's utility in drug development is demonstrated through integration in various stages, predicting drug responses, optimizing dosing, and evaluating combination therapies. Challenges exist in model complexity, communication, and peer review. Standardized workflows and evaluation methods ensure reliability and transparency.

Licensing: pros and cons for biotech

This article guides the reader through strategic considerations when facing the option to license a drug development project. It is crucial to understand these licensing events in their full complexity in order to achieve maximum value for the company and the shareholders, while minimizing risk. First, the nature of various license agreements and the needs of licensor and licensee are discussed. Second, the main strategic issues for the licensor are explained and a guideline, how to come to a decision whether to license and to what terms, is given. Third, the authors explain how to overcome different assumptions when negotiating a license contract.

Upstream ethics in nanomedicine: a call for research

Insufficient attention has been given to ethical and social issues integral to nanomedicine. Part of this deficiency arises from some mistaken assumptions about ethics. I consider five of these: that ethics is only important when a technology is mature (reactionary ethics); that there are no new ethical issues in nanomedicine; that ethics involves a kind of risk assessment that is already being conducted; that ethics is a hindrance to science; and that ethics is a luxury for an ideal world. After critically assessing these assumptions, I consider two types of nanomedicine and the kinds of ethical issues they raise. Type 1 nanomedicine is of an incremental kind, and proper ethical assessment of the issues must involve a fine grained study of the specific application. Type 2 nanomedicine is of a more foundational, programmatic kind. Ethical issues raised by these more programmatic developments include challenges integral to formation of interdisciplinary teams; issues related to intellectual property, authorship and publication; development of informed consent and confidentiality protections associated with new data sets; future challenges to the clinician–patient relation and personalized medicine. Ethical analysis should also consider some of the reductionistic implications of engineering models and metaphors integral to nanomedicine, as well as uses of nanomedicine for non-medical purposes, such as human enhancement. Many of these challenges concern rate-limiting steps in nanomedical research, and they should be prominently featured in developing nanomedicine initiatives.

The study of socioethical issues in systems biology

Systems biology is the rapidly growing and heavily funded successor science to genomics. Its mission is to integrate extensive bodies of molecular data into a detailed mathematical understanding of all life processes, with an ultimate view to their prediction and control. Despite its high profile and widespread practice, there has so far been almost no bioethical attention paid to systems biology and its potential social consequences. We outline some of systems biology's most important socioethical issues by contrasting the concept of systems as dynamic processes against the common static interpretation of genomes. New issues arise around systems biology's capacities for in silico testing, changing cultural understandings of life, synthetic biology, and commercialization. We advocate an interdisciplinary and interactive approach that integrates social and philosophical analysis and engages closely with the science. Overall, we argue that systems biology socioethics could stimulate new ways of thinking about socioethical studies of life sciences.

Applying computational modeling to drug discovery and development

Computational models of cells, tissues and organisms are necessary for increased understanding of biological systems. In particular, modeling approaches will be crucial for moving biology from a descriptive to a predictive science. Pharmaceutical companies identify molecular interventions that they predict will lead to therapies at the organism level, suggesting that computational biology can play a key role in the pharmaceutical industry. We discuss pharmaceutically-relevant computational modeling approaches currently used as predictive tools. Specific examples demonstrate how companies can employ these computational models to improve the efficiency of transforming targets into therapies.

Yeast systems biology to unravel the network of life

Systems biology focuses on obtaining a quantitative description of complete biological systems, even complete cellular function. In this way, it will be possible to perform computer-guided design of novel drugs, advanced therapies for treatment of complex diseases, and to perform in silico design of advanced cell factories for production of fuels, chemicals, food ingredients and pharmaceuticals. The yeast Saccharomyces cerevisiae represents an excellent model system; the density of biological information available on this organism allows it to serve as a eukaryotic model for studying human diseases. Furthermore, it serves as an industrial workhorse for production of a wide range of chemicals and pharmaceuticals. Systems biology involves the combination of novel experimental techniques from different disciplines as well as functional genomics, bioinformatics and mathematical modelling, and hence no single laboratory has access to all the necessary competences. For this reason the Yeast Systems Biology Network (YSBN) has been established. YSBN will coordinate research efforts in yeast systems biology and, through the recently obtained EU funding for a Coordination Action, it will be possible to set appropriate guidelines, establish an appropriate infrastructure for the network and organize courses, meetings and conferences that will consolidate the network and promote systems biology. This paper discusses the impacts of systems biology and how YSBN may play a role in the future development of the field.

Expression biomarkers for clinical efficacy and outcome prediction in cancer

Progress in cancer treatment has been slow, and the outlook for curing cancer is only marginally different from the situation a decade ago. Paradoxically, although the pharmaceutical industry has stepped up costly discovery research and drug development, approvals are on the decline and pipelines are dwindling. In an effort to reduce the number of drug failures and curtail burgeoning R&D costs, drug companies are exploring the use of biomarkers to evaluate toxicity and efficacy earlier in the development process. Biomarkers hold promise for optimization in dosing, adverse event prediction, efficacy evaluation, lead prioritization, and mechanism-of-action profiling of drug candidates. Furthermore, clinicians can use biomarkers to monitor patient response in clinical trials. In this perspective article, the authors explore the applications of cancer-related expression biomarkers in drug discovery and discuss how this will impact the industry and benefit the patient.

Systems modeling: a pathway to drug discovery

Systems modeling is emerging as a valuable tool in therapeutics. This is seen by the increasing use of clinically relevant computational models and a rise in systems biology companies working with the pharmaceutical industry. Systems models have helped understand the effects of pharmacological intervention at receptor, intracellular and intercellular communication stages of cell signaling. For instance, angiogenesis models at the ligand-receptor interaction level have suggested explanations for the failure of therapies for cardiovascular disease. Intracellular models of myeloma signaling have been used to explore alternative drug targets and treatment schedules. Finally, modeling has suggested novel approaches to treating disorders of intercellular communication, such as diabetes. Systems modeling can thus fill an important niche in therapeutics by making drug discovery a faster and more systematic process.

Combinatorial biomarkers: From early toxicology assays to patient population profiling

Advanced biomarkers for improved prediction and monitoring of disease and toxicology mechanisms are needed to control the high clinical failure rates among new compounds. Along with other uses, biomarkers are currently used in the industry to screen for toxic side effects of drug candidates and to identify appropriate patient populations. The use of combinatorial biomarkers is a first step towards an effective systems biology approach to drug development. In combination with high content screening, and in particular a novel, high content in situ proteomic technology, combinatorial biomarkers will strongly support the knowledge-based decision-making process by providing crucial information on functional biology.