Concept: Drug discovery
Medicinal plants have historically proven their value as a source of molecules with therapeutic potential, and nowadays still represent an important pool for the identification of novel drug leads. In the past decades, pharmaceutical industry focused mainly on libraries of synthetic compounds as drug discovery source. They are comparably easy to produce and resupply, and demonstrate good compatibility with established high throughput screening (HTS) platforms. However, at the same time there has been a declining trend in the number of new drugs reaching the market, raising renewed scientific interest in drug discovery from natural sources, despite of its known challenges. In this survey, a brief outline of historical development is provided together with a comprehensive overview of used approaches and recent developments relevant to plant-derived natural product drug discovery. Associated challenges and major strengths of natural product-based drug discovery are critically discussed. A snapshot of the advanced plant-derived natural products that are currently in actively recruiting clinical trials is also presented. Importantly, the transition of a natural compound from a “screening hit” through a “drug lead” to a “marketed drug” is associated with increasingly challenging demands for compound amount, which often cannot be met by re-isolation from the respective plant sources. In this regard, existing alternatives for resupply are also discussed, including different biotechnology approaches and total organic synthesis. While the intrinsic complexity of natural product-based drug discovery necessitates highly integrated interdisciplinary approaches, the reviewed scientific developments, recent technological advances, and research trends clearly indicate that natural products will be among the most important sources of new drugs also in the future.
Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.
- Proceedings of the National Academy of Sciences of the United States of America
- Published over 7 years ago
A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are nonexistent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g., R(2) between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.
Natural products have always been exploited to promote health and served as a valuable source for the discovery of new drugs. In this review, the great potential of natural compounds and medicinal plants for the treatment or prevention of cardiovascular and metabolic disorders, global health problems with rising prevalence, is addressed. Special emphasis is laid on natural products for which efficacy and safety have already been proven and which are in clinical trials, as well as on plants used in traditional medicine. Potential benefits from certain dietary habits and dietary constituents, as well as common molecular targets of natural products, are also briefly discussed. A glimpse at the history of statins and biguanides, two prominent representatives of natural products (or their derivatives) in the fight against metabolic disease, is also included. The present review aims to serve as an “opening” of this special issue of Molecules, presenting key historical developments, recent advances, and future perspectives outlining the potential of natural products for prevention or therapy of cardiovascular and metabolic disease.
Options for leveraging available telemedicine technologies, ranging from simple webcams and telephones to smartphone apps and medical-grade wearable sensors, are evolving faster than the culture of clinical research. Until recently, most clinical trials relied on paper-based processes and technology. This cost- and labor-intensive system, while slowly changing, remains an obstacle to new drug development. Alternatives that use existing tools and processes for collecting real-world data in home settings warrant closer examination.
Medicinal chemists' “intuition” is critical for success in modern drug discovery. Early in the discovery process, chemists select a subset of compounds for further research, often from many viable candidates. These decisions determine the success of a discovery campaign, and ultimately what kind of drugs are developed and marketed to the public. Surprisingly little is known about the cognitive aspects of chemists' decision-making when they prioritize compounds. We investigate 1) how and to what extent chemists simplify the problem of identifying promising compounds, 2) whether chemists agree with each other about the criteria used for such decisions, and 3) how accurately chemists report the criteria they use for these decisions. Chemists were surveyed and asked to select chemical fragments that they would be willing to develop into a lead compound from a set of ∼4,000 available fragments. Based on each chemist’s selections, computational classifiers were built to model each chemist’s selection strategy. Results suggest that chemists greatly simplified the problem, typically using only 1-2 of many possible parameters when making their selections. Although chemists tended to use the same parameters to select compounds, differing value preferences for these parameters led to an overall lack of consensus in compound selections. Moreover, what little agreement there was among the chemists was largely in what fragments were undesirable. Furthermore, chemists were often unaware of the parameters (such as compound size) which were statistically significant in their selections, and overestimated the number of parameters they employed. A critical evaluation of the problem space faced by medicinal chemists and cognitive models of categorization were especially useful in understanding the low consensus between chemists.
- Journal of clinical oncology : official journal of the American Society of Clinical Oncology
- Published almost 5 years ago
The use of biopsy-derived pharmacodynamic biomarkers is increasing in early-phase clinical trials. It remains unknown whether drug development is accelerated or enhanced by their use. We examined the impact of biopsy-derived pharmacodynamic biomarkers on subsequent drug development through a comprehensive analysis of phase I oncology studies from 2003 to 2010 and subsequent publications citing the original trials.
Fatty acid binding proteins (FABPs), in particular FABP5 and FABP7, have recently been identified by us as intracellular transporters for the endocannabinoid anandamide (AEA). Furthermore, animal studies by others have shown that elevated levels of endocannabinoids resulted in beneficial pharmacological effects on stress, pain and inflammation and also ameliorate the effects of drug withdrawal. Based on these observations, we hypothesized that FABP5 and FABP7 would provide excellent pharmacological targets. Thus, we performed a virtual screening of over one million compounds using DOCK and employed a novel footprint similarity scoring function to identify lead compounds with binding profiles similar to oleic acid, a natural FABP substrate. Forty-eight compounds were purchased based on their footprint similarity scores (FPS) and assayed for biological activity against purified human FABP5 employing a fluorescent displacement-binding assay. Four compounds were found to exhibit approximately 50% inhibition or greater at 10 µM, as good as or better inhibitors of FABP5 than BMS309403, a commercially available inhibitor. The most potent inhibitor, γ-truxillic acid 1-naphthyl ester (ChemDiv 8009-2334), was determined to have K(i) value of 1.19±0.01 µM. Accordingly a novel α-truxillic acid 1-naphthyl mono-ester (SB-FI-26) was synthesized and assayed for its inhibitory activity against FABP5, wherein SB-FI-26 exhibited strong binding (K(i) 0.93±0.08 µM). Additionally, we found SB-FI-26 to act as a potent anti-nociceptive agent with mild anti-inflammatory activity in mice, which strongly supports our hypothesis that the inhibition of FABPs and subsequent elevation of anandamide is a promising new approach to drug discovery. Truxillic acids and their derivatives were also shown by others to have anti-inflammatory and anti-nociceptive effects in mice and to be the active component of Chinese a herbal medicine (Incarvillea sinensis) used to treat rheumatism and pain in humans. Our results provide a likely mechanism by which these compounds exert their effects.
Human African Trypanosomiasis is a vector-borne disease of sub-Saharan Africa that causes significant morbidity and mortality. Current therapies have many drawbacks, and there is an urgent need for new, better medicines. Ideally such new treatments should be fast-acting cidal agents that cure the disease in as few doses as possible. Screening assays used for hit-discovery campaigns often do not distinguish cytocidal from cytostatic compounds and further detailed follow-up experiments are required. Such studies usually do not have the throughput required to test the large numbers of hits produced in a primary high-throughput screen. Here, we present a 384-well assay that is compatible with high-throughput screening and provides an initial indication of the cidal nature of a compound. The assay produces growth curves at ten compound concentrations by assessing trypanosome counts at 4, 24 and 48 hours after compound addition. A reduction in trypanosome counts over time is used as a marker for cidal activity. The lowest concentration at which cell killing is seen is a quantitative measure for the cidal activity of the compound. We show that the assay can identify compounds that have trypanostatic activity rather than cidal activity, and importantly, that results from primary high-throughput assays can overestimate the potency of compounds significantly. This is due to biphasic growth inhibition, which remains hidden at low starting cell densities and is revealed in our static-cidal assay. The assay presented here provides an important tool to follow-up hits from high-throughput screening campaigns and avoid progression of compounds that have poor prospects due to lack of cidal activity or overestimated potency.
Three dedicated approaches to the calculation of the risk-adjusted net present value (rNPV) in drug discovery projects under different assumptions are suggested. The probability of finding a candidate drug suitable for clinical development and the time to the initiation of the clinical development is assumed to be flexible in contrast to the previously used models. The rNPV of the post-discovery cash flows is calculated as the probability weighted average of the rNPV at each potential time of initiation of clinical development. Practical considerations how to set probability rates, in particular during the initiation and termination of a project is discussed.