scholarly journals A scientist engineer’s contribution to therapeutic discovery and development

2018 ◽  
Vol 243 (14) ◽  
pp. 1125-1132 ◽  
Author(s):  
Jennifer L Wilson

An engineering perspective views cells as complex circuits that process inputs – drugs, environmental cues – to create complex outcomes – disease, growth, death – and this perspective has immense potential for drug development. Logical rules can describe the features of cells and reductionist approaches have exploited these rules for drug development. In contrast, the reductionist approach serially characterizes cellular components and develops a deep understanding of each component’s specific role. This approach underutilizes the full system of biomolecules relevant to disease pathology and drug effects. An engineering perspective provides the tools to understand and leverage the full extent of biological systems; applying both reverse and forward engineering, a strength of the engineering approach has demonstrated progress in advancing understanding of disease and drug mechanisms. Drug development lacks sufficient engineering specifications, or empirical models, of drug pharmacodynamic effects and future efforts to derive empirical models of drug effects will streamline this development. At this stage of progress, the scientist engineer is uniquely poised to solve problems in therapeutics related to modulating multiple diseases with a single or multiple therapeutic agents and identifying pharmacodynamics biomarkers with knowledge of drug pathways. This article underscores the value of these principles in an age where drug development costs are soaring and finding efficacious therapies is challenging. Impact statement Many untreated diseases are not monogenic and are instead caused by multiple genetic defects. Because of this complexity, computational, logical, and systems understanding will be essential to discovering novel therapies. The scientist engineer is uniquely disposed to use this type of understanding to advance therapeutic discovery. This work highlights benefits of the scientist engineer perspective and underscores the potential impact of these approaches for future therapeutic development. By framing the scientist engineer’s tool set and increasing awareness about this approach, this article stands to impact future therapeutic development efforts in an age of rising development costs and high drug attrition.

Author(s):  
M.B. Isaac ◽  
S. Vamvakas

Despite substantial advances in the understanding of central nervous system (CNS) disorders, healthcare systems worldwide face an unprecedented challenge in dealing with the unmet needs in this area (1). Meanwhile, the CNS drug pipeline looks worryingly dry. There are several reasons for this, including the obvious complexity of the CNS, a lack of interdisciplinary collaborations, increased drug development costs and the higher risk of clinical failure of CNS drugs, compared with those in other areas of drug development. The year 2016 was also disappointing in terms of failed trials of Alzheimer’ Dementia (AD) drugs.


2003 ◽  
Vol 22 (2) ◽  
pp. 325-330 ◽  
Author(s):  
Richard G Frank

2015 ◽  
Vol 21 (4) ◽  
Author(s):  
Ajay Gautam

The article proposes a “virtual” biotech model for the emerging markets - termed EmergingCo - and develops a comparative financial model to argue that such a virtual biotech can deliver drug candidates from discovery through proof-of-concept (Phase II) more cost effectively than the traditional drug development paradigm. Data from published studies on drug development costs have been compared with a cost structure model for EmergingCo using a framework where all R&D can be accomplished through a virtual network of partnerships within emerging markets. A couple of case studies from China and India are used to lend support to the cost structure model. Such a model, either as a venture backed company or a virtual unit of big pharma, could provide an alternate vehicle for delivering mid-to late stage clinical candidates, similar to Lilly's Chorus model.


2018 ◽  
Vol 18 (20) ◽  
pp. 1745-1754 ◽  
Author(s):  
Sneha Rai ◽  
Utkarsh Raj ◽  
Pritish Kumar Varadwaj

The conventional way of characterizing a disease consists of correlating clinical symptoms with pathological findings. Although this approach for many years has assisted clinicians in establishing syndromic patterns for pathophenotypes, it has major limitations as it does not consider preclinical disease states and is unable to individualize medicine. Moreover, the complexity of disease biology is the major challenge in the development of effective and safe medicines. Therefore, the process of drug development must consider biological responses in both pathological and physiological conditions. Consequently, a quantitative and holistic systems biology approach could aid in understanding complex biological systems by providing an exceptional platform to integrate diverse data types with molecular as well as pathway information, leading to development of predictive models for complex diseases. Furthermore, an increase in knowledgebase of proteins, genes, metabolites from high-throughput experimental data accelerates hypothesis generation and testing in disease models. The systems biology approach also assists in predicting drug effects, repurposing of existing drugs, identifying new targets, facilitating development of personalized medicine and improving decision making and success rate of new drugs in clinical trials.


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