This course considers the process of neurotransmission, especially chemicals used in the brain and elsewhere to carry signals from nerve terminals to the structures they innervate. We focus on monoamine transmitters (acetylcholine; serotonin; dopamine and norepinephrine); we also examine amino acid and peptide transmitters and neuromodulators like adenosine. Macromolecules that mediate neurotransmitter synthesis, release, inactivation and receptor-mediated actions are discussed, as well as factors that regulate their activity and the second-messenger systems and ion fluxes that they control. The involvement of particular neurotransmitters in human diseases is considered.

This course teaches the design of contemporary information systems for biological and medical data. Examples are chosen from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (e.g. C, C++, Java, Lisp, Perl, Python). A major term project is required of all students. This subject is open to motivated seniors having a strong interest in biomedical engineering and information system design with the ability to carry out a significant independent project.
This course was offered as part of the Singapore-MIT Alliance (SMA) program as course number SMA 5304.

We can regard the wider incentive structures that operate across science, such as the priority given to novel findings, as an ecosystem within which scientists strive to maximise their fitness (i.e., publication record and career success). Here, we develop an optimality model that predicts the most rational research strategy, in terms of the proportion of research effort spent on seeking novel results rather than on confirmatory studies, and the amount of research effort per exploratory study. We show that, for parameter values derived from the scientific literature, researchers acting to maximise their fitness should spend most of their effort seeking novel results and conduct small studies that have only 10%–40% statistical power. As a result, half of the studies they publish will report erroneous conclusions. Current incentive structures are in conflict with maximising the scientific value of research; we suggest ways that the scientific ecosystem could be improved.

In this capstone course, students will use new and previous knowledge about drug delivery and biopharmaceutics, to design an innovation. Throughout the course students will engage in learning opportunities related to real-world scenarios in drug delivery, gain a better understanding of the anatomy and physiology related to drug delivery, and participate in a self-directed project to solve a fictitious problem. This learning tool will guide students through the process of understanding real-world applications of drug delivery and how drug delivery is applied to treating infectious diseases. DDF’s innovation project is aligned with NGSS and Common Core standards in math and ELA core curriculum subject areas. The learning activities, final project, and mid-unit assessments are provided to the teacher and students in the form of eLearning readings, quizzes, interactive tools, student response sheets, and presentation outlines. Students using this module should find success in self-directed learning, though they may use additional resources in the community, the guidance of teachers, the advice of scientists or biomedical professionals at DDF, or the knowledge presented in scientific literature to help them achieve their goal; though this module should provide most of the tools they will need for guidance. For more information on in-person learning experiences, please contact our DDF eLearning Project Manager, Lindsay Malcolm: lmalcolm@tsrlinc.com

Low reproducibility rates within life science research undermine cumulative knowledge production and contribute to both delays and costs of therapeutic drug development. An analysis of past studies indicates that the cumulative (total) prevalence of irreproducible preclinical research exceeds 50%, resulting in approximately US$28,000,000,000 (US$28B)/year spent on preclinical research that is not reproducible—in the United States alone. We outline a framework for solutions and a plan for long-term improvements in reproducibility rates that will help to accelerate the discovery of life-saving therapies and cures.

This course will assess the relationships among sequence, structure, and function in complex biological networks as well as progress in realistic modeling of quantitative, comprehensive, functional genomics analyses. Exercises will include algorithmic, statistical, database, and simulation approaches and practical applications to medicine, biotechnology, drug discovery, and genetic engineering. Future opportunities and current limitations will be critically addressed. In addition to the regular lecture sessions, supplementary sections are scheduled to address issues related to Perl, Mathematica and biology.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Kinetoplastids are microscopic flagellated protozoans. They may not look like much, but these parasites are responsible for diseases that affect millions of people worldwide including Leishmaniasis, Sleeping Sickness, and Chagas Disease. Despite their role in global health concerns, however, effective drug treatments for these parasites are lacking. While different parasites are responsible for each of these conditions and they are transmitted by different vectors, the cellular and molecular biology of Kinetoplastids are quite similar. Taking advantage of this, an international team of scientists has developed three new anti-parasitic compound groups to combat these devastating microorganisms. By screening nearly 2 million compounds against the three target kinetoplastids, the team was able to identify those with potential anti-parasitic effects. They then characterized these molecules’ chemical and physiological properties and grouped them by their potential use for each disease..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

There is a scarcity of novel treatments to address many unmet medical needs. Industry and academia are finally coming to terms with the fact that the prevalent models and incentives for innovation in early stage drug discovery are failing to promote progress quickly enough. Here we will examine how an open model of precompetitive public–private research partnership is enabling efficient derisking and acceleration in the early stages of drug discovery, whilst also widening the range of communities participating in the process, such as patient and disease foundations.

This course serves as a description and critical assessment of the major issues and stages of developing a pharmaceutical or biopharmaceutical. Topics covered include drug discovery, preclinical development, clinical investigation, manufacturing and regulatory issues considered for small and large molecules, and economic and financial considerations of the drug development process. A multidisciplinary perspective is provided by the faculty, who represent clinical, life, and management sciences. Various industry guests also participate.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Immune checkpoint blockade has enabled great progress in cancer immunotherapy. TIGIT is a novel immune checkpoint that plays a critical role in cancer immunity. TIGIT’s inhibitory function relies on the molecule’s interaction with the ligand PVR. That makes blocking the TIGIT/PVR interaction a promising therapeutic approach. But small molecules suited for the job have not yet been investigated. Now, research points to the benefits of the molecule liothyronine. Liothyronine is an artificial hormone used to treat hypothyroidism. A combination of molecular docking, screening, and a series of in vitro and in vivo assays revealed that liothyronine could be a suitable TIGIT/PVR blocker. When applied to tumor-bearing mice, liothyronine significantly inhibited tumor growth by enhancing CD8+ T cell infiltration and immune responses..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This learning tool will guide students through the process of understanding real-world applications of drug delivery and how drug delivery is applied to treating infectious diseases. Students using this module should find success in self-directed learning, though they may use additional resources in the community, the guidance of teachers, the advice of scientists or biomedical professionals at DDF, or the knowledge presented in scientific literature to help them achieve their goal; though this module should provide most of the tools they will need for guidance.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Researchers working in Australia have uncovered why highly similar drugs sometimes produce very different responses. Understanding these variations is key to developing drugs with a strong therapeutic effect and minimal side effects. Generally, drugs work by interacting with specific proteins - known as receptors – on the surfaces of cells. These interactions cause changes in cell signaling that allow the body to respond to medicine. The overall goal of drug design is to create molecules that bind to specific receptors to cause distinct changes in cells. But molecules designed to bind to the same receptor can affect cells in unexpected ways. These unintended effects can result from biased agonism. This occurs when drugs designed to target the same receptor end up binding to the receptor at slightly different positions, causing activation of unique signaling pathways. This bias offers a potential therapeutic advantage by allowing preferential activation of beneficial versus harmful cellular responses..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

Background The increased use of meta-analysis in systematic reviews of healthcare interventions has highlighted several types of bias that can arise during the completion of a randomised controlled trial. Study publication bias and outcome reporting bias have been recognised as a potential threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making. Methodology/Principal Findings In this update, we review and summarise the evidence from cohort studies that have assessed study publication bias or outcome reporting bias in randomised controlled trials. Twenty studies were eligible of which four were newly identified in this update. Only two followed the cohort all the way through from protocol approval to information regarding publication of outcomes. Fifteen of the studies investigated study publication bias and five investigated outcome reporting bias. Three studies have found that statistically significant outcomes had a higher odds of being fully reported compared to non-significant outcomes (range of odds ratios: 2.2 to 4.7). In comparing trial publications to protocols, we found that 40–62% of studies had at least one primary outcome that was changed, introduced, or omitted. We decided not to undertake meta-analysis due to the differences between studies. Conclusions This update does not change the conclusions of the review in which 16 studies were included. Direct empirical evidence for the existence of study publication bias and outcome reporting bias is shown. There is strong evidence of an association between significant results and publication; studies that report positive or significant results are more likely to be published and outcomes that are statistically significant have higher odds of being fully reported. Publications have been found to be inconsistent with their protocols. Researchers need to be aware of the problems of both types of bias and efforts should be concentrated on improving the reporting of trials.

In an Essay, Matthew Todd and colleagues discuss an open source approach to drug development. This Essay outlines how open source methods of working could be applied to the discovery and development of new medicines. There are many potential advantages of an open source approach, such as improved efficiency, the quality and relevance of the research, and wider participation by the scientific and patient communities; a blend of traditional and innovative financing mechanisms will have to be adopted. To evaluate properly the effectiveness of an open source methodology and its potential as an alternative model of drug discovery and development, we recommend that new projects be trialed and existing projects scaled up. Where we stand The scientific and medical community has discovered and developed many groundbreaking medicines that have had a major impact on public health. However, drug development is challenged by a widening gap between health needs and the pharmaceutical industry’s motives and business model, alongside a decrease in efficiency per research dollar spent in medicinal product research and development (R&D), a trend known colloquially as Eroom’s Law. Such fundamental challenges result in frequent high-level calls for new initiatives to develop therapeutics and bring them to market. These include market push and pull mechanisms such as priority review vouchers, advance market commitments, and public R&D funding. New organizational models have also emerged, including public–private partnerships (PPPs) and not-for-profit product development partnerships (PDPs) (for example, the Drugs for Neglected Diseases Initiative [DNDi], the Medicines for Malaria Venture [MMV], and the Global Alliance for Tuberculosis Drug Development [TB Alliance]) that often apply a full “de-linkage” model in which the price of medicines and the cost of R&D are uncoupled.