Drugs for Neglected Diseases
            <i>Initiative</i>
            Model of Drug Development for Neglected Diseases: Current Status and Future Challenges

Ioset, Jean‐Robert; Chang, Shing

doi:10.4155/fmc.11.102

Cited by 26 publications

(31 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following from this, a new dose algorithm was developed, resulting in similar levels of systemic exposure to miltefosine between Indian children and Indian adults. The treatment of VL patients has improved over the past years, as the international scientific attention has increased and several not-for-profit organizations have made it a priority to develop new chemical entities, drugs, and combination treatments for this fatal neglected disease (10,13,24,61). Unfortunately, the currently available drugs for treatment of VL featured several lacunas during their development, which may have been in part due to the difficulty of performing clinical trials in the resource-limited settings where VL is present.…”

Section: Discussionmentioning

confidence: 99%

Optimal Dosing of Miltefosine in Children and Adults with Visceral Leishmaniasis

Dorlo

Huitema

Beijnen

et al. 2012

Antimicrob Agents Chemother

View full text Add to dashboard Cite

bOnly anecdotal data are available on the pharmacokinetics (PK) of miltefosine in children suffering from visceral leishmaniasis (VL). While failure rates were higher in children with VL, steady-state concentrations appeared lower than those seen with adults. We hypothesized that the current linear dosage (in milligrams per kilogram of body weight) is too low for children and that a new dosing algorithm based on an appropriate body size model would result in an optimal exposure. A population PK analysis was performed on three historic pooled data sets, including Indian children, Indian adults, and European adults. Linear and allometric scaling of PK parameters by either body weight or fat-free mass (FFM) was evaluated for body size models. Based on the developed PK model, a dosing algorithm for miltefosine in children and adults was proposed and evaluated in silico. The population PK model employing allometric scaling fitted best to the pooled miltefosine data. Allometric scaling by FFM reduced between-subject variability, e.g., for drug clearance, from 49.6% to 32.1%. A new allometric miltefosine dosing algorithm was proposed. Exposure to miltefosine was lower in children than adults receiving 2.5 mg/kg/day: a C max of 18.8 g/ml was reached by 90% of adults and 66.7% of children. The allometric daily dose resulted in similar levels of exposure to miltefosine for adults and children. The use of a new allometric dosing algorithm for miltefosine in VL patients results in optimal exposure to miltefosine in both adults and children and might improve clinical outcome in children.V isceral leishmaniasis (VL) or kala-azar (Hindi for "black fever") has been classified as one of the world's "most neglected diseases" (62), and of all the parasitic diseases, it ranks third in terms of morbidity and mortality after only malaria and lymphatic filariasis (38). The World Health Organization (WHO) reported in 2004 that leishmaniasis is responsible for around 50,000 deaths and almost 2 million disability-adjusted life-years per year, approximately half of which can be attributed to children from lowincome countries (38, 60), but in reality the burden of leishmaniasis is probably much larger due to massive underreporting of both cases and deaths in the often remote areas where leishmaniasis is prevalent (47).Miltefosine (hexadecylphosphocholine; marketed by Paladin Laboratories Inc. as Impavido) is the newest addition to the small repertory of antileishmanial drugs and to date is the only drug that can be administered orally. It has achieved relatively high cure rates in the treatment of visceral (8, 26, 41, 52, 54), New World cutaneous (11,44,48,50,57), Old World cutaneous (30, 56) and even difficult-to-treat mucocutaneous (49, 51) leishmaniasis.During the development of miltefosine, two clinical trials were designed to investigate the efficacy and safety of miltefosine in children (Ͻ12 years of age) in which a total of 119 pediatric patients were enrolled, employing dosages linearly extrapolated from the daily adult dosage (in ...

show abstract

Section: Discussionmentioning

confidence: 99%

Optimal Dosing of Miltefosine in Children and Adults with Visceral Leishmaniasis

Dorlo

Huitema

Beijnen

et al. 2012

Antimicrob Agents Chemother

View full text Add to dashboard Cite

show abstract

“…Developing a new drug is however slow and expensive. The average cost to bring a new drug to market is > 2.5 billion US dollars (DiMasi et al 2015), which means that tropical diseases such as malaria, schistosomiasis, Chagas' disease, etc., which kill millions of people and infect hundreds of millions of others are 'neglected' (Ioset and Chang 2011;Leslie and Inouye 2011) and that 'orphan' diseases (i.e. those with few sufferers) remain untreatable (Braun et al 2010).…”

Section: Quantitative Structure Activity Relationship (Qsar) Learningmentioning

confidence: 99%

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

et al. 2017

View full text Add to dashboard Cite

We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study of meta-learning. This application area is of the highest societal importance, as it is a key step in the development of new medicines. The standard QSAR learning problem Learn (2018) 107:285-311 is: given a target (usually a protein) and a set of chemical compounds (small molecules) with associated bioactivities (e.g. inhibition of the target), learn a predictive mapping from molecular representation to activity. Although almost every type of machine learning method has been applied to QSAR learning there is no agreed single best way of learning QSARs, and therefore the problem area is well-suited to meta-learning. We first carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 3 molecular representations, applied to more than 2700 QSAR problems. (These results have been made publicly available on OpenML and represent a valuable resource for testing novel meta-learning methods.) We then investigated the utility of algorithm selection for QSAR problems. We found that this meta-learning approach outperformed the best individual QSAR learning method (random forests using a molecular fingerprint representation) by up to 13%, on average. We conclude that meta-learning outperforms base-learning methods for QSAR learning, and as this investigation is one of the most extensive ever comparisons of base and meta-learning methods ever made, it provides evidence for the general effectiveness of meta-learning over base-learning.

show abstract

“…The current business model of the pharmaceutical industry is unable to meet all medical needs, especially in the areas of neglected and rare diseases. In response, a variety of open source models have been implemented by a variety of organizations [56][57][58] (http://opensourcemalaria.org/; http:// www.osdd.net/) and there has been some recent discussion regarding the viability of such an open source business model expansion beyond the area of neglected diseases [59,60] (http:// www.opensourcepharma.net/). An interesting approach for drug development is the crowdsourcing of clinical trial design [61,62].…”

Section: Concluding Remarks and Discussionmentioning

confidence: 99%

Crowdsourcing in pharma: a strategic framework

Bentzien¹,

Bharadwaj²,

Thompson³

2015

Drug Discovery Today

View full text Add to dashboard Cite

Drugs for Neglected Diseases Initiative Model of Drug Development for Neglected Diseases: Current Status and Future Challenges

Cited by 26 publications

References 19 publications

Optimal Dosing of Miltefosine in Children and Adults with Visceral Leishmaniasis

Optimal Dosing of Miltefosine in Children and Adults with Visceral Leishmaniasis

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

Crowdsourcing in pharma: a strategic framework

Contact Info

Product

Resources

About