Modeling and Prediction of Solvent Effect on Human Skin Permeability using Support Vector Regression and Random Forest

Abstract

Our high-performance prediction model offers an attractive alternative to permeability experiments for pharmaceutical and cosmetic candidate screening and optimizing skin-permeable topical formulations.

The Next Era: Deep Learning in Pharmaceutical Research

“…Based on a non-exhaustive assessment of several different end points relevant to pharmaceutical research, while it appears that most have seen utilization of Bayesian or SVM approaches to develop predictive models, few have so far utilized DL (Table 2). Recent examples of computational models appearing in this journal alone over the past 18 months include: modeling thermodynamic proxies (35), predicting mouse liver microsomal stability (36), predicting autooxidation (37), drug solubility in human intestinal fluid (38), site of metabolism prediction in CYP2C9 (39), human skin permeability prediction (40, 41), blood brain barrier penetration modeling (42), predicting clearance mechanism (41) and skin concentration due to dermal exposure (43). Many of these datasets could likely utilize and benefit from DL and it would be of interest to see for how many an improvement in predictions could be obtained.…”

Deep learning: from chemoinformatics to precision medicine

“…Moreover, in the area of drug formulation, a deep learning algorithm has predicted drug release from polylactide-coglycolide microspheres (Zawbaa et al 2016). Recently, computational models have been applied in the area of drug development such as studies modeling thermodynamic proxies , drug solubility simulation in human intestinal fluid (Fagerberg et al 2015), stability prediction in mouse liver microsomes (Perryman et al 2016), auto-oxidation predictions (Lienard et al 2015), sites of CYP2C9 metabolism prediction (Kingsley et al 2015), human skin permeability prediction (Baba et al 2015), blood-brain barrier penetration , and estimates of skin concentration levels after dermal exposure (Hatanaka et al 2015). Additionally, the absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties of molecules have been modeled by applying a variety of machine learning algorithms such as Bayesian modeling, (Klon et al 2006), Gaussian processes (Obrezanova et al 2007), and support vector machines (Zheng et al 2009).…”

Descriptors for ions and ion-pairs for use in linear free energy relationships

“…(1), where SP = log Kp (cm s −1 ) has been applied to the permeation of human skin from aqueous solution [86][87][88]. Numerous other studies on skin permeation have been reported but recent papers [89] and recent reviews [90] do not even mention the possibility of partition of ionic species. Zhang et al [91] determined Kp values for acids and bases at values of pH where they existed in the neutral form and at values of pH where they were in the ionised form.…”