A drug-incorporated-microparticle-eggshell-membrane-scaffold (DIMES) dressing: A novel biomaterial for localised wound regeneration.

Chronic wounds affect millions of people annually and have emotional and financial implications in addition to health issues. The current treatment for chronic wounds involves the repeated use of bandages and drugs such as antibiotics over an extended period. A cost-effective and convenient solution for wound healing is the development of drug-incorporated bandages. This study aimed to develop a biocompatible bandage made of drug-incorporated poly (lactic-co-glycolic acid) (PLGA) microparticles (MPs) and eggshell membrane (ESM) for cornea wound healing. ESM has desirable properties for wound healing and can be isolated from eggshells using acetic acid or ethylenediaminetetraacetic acid (EDTA) protocols. Fluorescein isothiocyanate-labelled Bovine Serum Albumin (FITC-BSA) was used as a model drug, and the PLGA MPs were fabricated using a solvent extraction method. The MPs were successfully attached to the fibrous layer of the ESM using NaOH. The surface features of the ESM samples containing MPs were studied using a field emission scanning electron microscope (FESEM) and compared with blank ESM images. The findings indicated that the MPs were attached to the ESM fibres and had similar shapes and sizes as the control MPs. The fibre diameters of the MPs samples were assessed using Fiji-ImageJ software, and no significant changes were observed compared to the blank ESM. The surface roughness, Ra values, of the MPs incorporated ESM samples were evaluated and compared to the blank ESM, and no significant changes were found. Fourier transform infrared (FTIR) spectroscopy was used to analyse the chemical Composition of the bandage, and the spectra showed that the FBM were effectively incorporated into the ESM. The FTIR spectra identified the major peaks of the natural ESM and the PLGA polymer in the bandage. The bandage was transparent but had a reduced visibility in the waterproof test card method. The bandage achieved sustained drug release up to 10 days and was found to be biocompatible and non-toxic in a chorioallantoic membrane (CAM) assay. Overall, the drug-incorporated PLGA MPs-ESM bandage has great potential for treating chronic wounds.

In Silico Investigations into the Selectivity of Psychoactive and New Psychoactive Substances in Monoamine Transporters.

New psychoactive substances (NPS) are a group of compounds that mimic the effects of illicit substances. A range of NPS have been shown to interact with the three main classes of monoamine transporters (DAT, NET, and SERT) to differing extents, but it is unclear why these differences arise. To aid in understanding the differences in affinity between the classes of monoamine transporters, several in silico experiments were conducted. Docking experiments showed there was no direct correlation between a range of scoring functions and experimental activity, but Spearman ranking analysis showed a significant correlation (α = 0.1) for DAT, with the affinity ΔG (0.42), αHB (0.40), GoldScore (0.40), and PLP (0.41) scoring functions, and for DAT (0.38) and SERT (0.40) using a consensus scoring approach. Qualitative structure-activity relationship (QSAR) experiments resulted in the generation of robust and predictive three-descriptor models for SERT (r 2 = 0.87, q 2 = 0.8, and test set r 2 = 0.74) and DAT (r 2 = 0.68, q 2 = 0.51, test set r 2 = 0.63). Both QSAR models described similar characteristics for binding, i.e., rigid hydrophobic molecules with a biogenic amine moiety, and were not sufficient to facilitate a deeper understanding of differences in affinity between the monoamine transporters. This contextualizes the observed promiscuity for NPS between the isoforms and highlights the difficulty in the design and development of compounds that are isoform-selective.

NPS detection in prison: A systematic literature review of use, drug form, and analytical approaches.

This paper presents a systematic literature review on the detection of new psychoactive substances (NPS) in prison settings. It includes the most frequently reported NPS classes, the routes and forms used for smuggling, and the methods employed to analyse biological and non-biological samples. The search was carried out using MEDLINE (EBSCO), Scopus (ELSEVIER), PubMed (NCBI), and Web of Science (Clarivate) databases, along with reports from the grey literature in line with the PRISMA-S guidelines. A total of 2708 records were identified, of which 50 met the inclusion criteria. Findings showed the most prevalent NPS class reported in prison was synthetic cannabinoids (SCs). The most frequently reported SCs in non-biological samples were 4F-MDMB-BINACA, MDMB-4en-PINACA, and 5F-ADB. These were smuggled mainly through the postal services deposited on paper or herbal matrices. Concentrations of SCs detected on seized paper ranged between 0.05 and 1.17 mg/cm2 . The SCs most frequently reported in biological specimens (i.e., urine, blood, saliva, and wastewater) were 5F-MDMB-PICA, 4F-MDMB-BINACA, and MDMB-4en-PINACA. Concentrations of SCs reported in femoral blood and serum were 0.12-0.48 ng/ml and 34-17 ng/ml, respectively. Hyphenated techniques were predominantly employed and generally successful for the detection of NPS in biological (i.e., LC-HRMS/MS) and non-biological samples (i.e., LC-HRMS/MS and GC-MS). The onsite technique IMS showed promise for detecting SCs in various forms; however, immunoassays were not recommended. Future work should focus on accurate in-field detection of SCs deposited on paper and in urine and saliva to improve real-time decision-making, as well as wastewater and air monitoring for overall drug use trends.

Polymer Architecture Effects on Poly(N,N-Diethyl Acrylamide)-b-Poly(Ethylene Glycol)-b-Poly(N,N-Diethyl Acrylamide) Thermoreversible Gels and Their Evaluation as a Healthcare Material.

Thermoreversible gels which transition between liquid-like and solid-like states when warmed have enabled significant novel healthcare technologies. Poly(N,N-diethyl acrylamide) (PDEA) is a thermoresponsive polymer which can be used as a trigger to form thermoreversible gels, however its use in these materials is limited and crucial design principles are unknown. Herein ABA copolymers with the structure PDEA-b-poly(ethylene glycol) (PEG)-b-PDEA are synthesized to give four block copolymers with varied molecular weight of PDEA and PEG blocks. Rheometry on solutions of the block copolymers reveals that high molecular weight PEG blocks are required to form thermoreversible gels with predominantly solid-like behavior. Furthermore, small-angle X-ray scattering elucidates clear differences in the nanostructure of the copolymer library which can be linked to distinct rheological behaviors. A thermoreversible gel formulation based on PDEA (20 kDa)-b-PEG (10 kDa)-b-PDEA (20 kDa) is designed by optimizing the polymer concentration and ionic strength. It is found that the gel is mucoadhesive, stable, and non-toxic, as well as giving controlled release of a hydrophobic drug. Overall, this study provides insight into the effect of polymer architecture on the nanostructure and rheology of PDEA-b-PEG-b-PDEA and presents the development of a highly functional thermoreversible gel with high promise for healthcare applications.

The eggshell membrane: A potential biomaterial for corneal wound healing.

The eggshell membrane (ESM) is an abundant resource with innate complex structure and composition provided by nature. With at least 60 million tonnes of hen eggs produced globally per annum, utilisation of this waste resource is highly attractive in positively impacting sustainability worldwide. Given the morphology and mechanical properties of this membrane, it has great potential as a biomaterials for wound dressing. However, to date, no studies have demonstrated nor reported this application. As such, the objective of this investigation was to identify and optimise a reproducible extraction protocol of the ESM and to assess the physical, chemical, mechanical and biological properties of the substrate with a view to use as a wound dressing. ESM samples were isolated by either manual peeling (ESM-strip) or via extraction using acetic acid [ESM-A0.5] or ethylenediaminetetraacetic acid, EDTA [ESM-E0.9]. Energy dispersive X-ray spectroscopy (EDS) confirmed that there were no traces of calcium residues from the extraction process. Fourier transform infrared (FTIR) spectroscopy revealed that the extraction method (acetic acid and EDTA) did not alter the chemical structures of the ESM and also clarified the composition of the fibrous proteins of the ESM. Scanning electron microscopy (SEM) analyses revealed a three-layer composite structure of the ESM: an inner layer as continuous, dense and non-fibrous (limiting membrane), a middle layer with a network of fibres (inner shell membrane) and the outer layer (outer shell membrane) of larger fibres. Material properties including optical transparency, porosity, fluid absorption/uptake, thermal stability, mechanical profiling of the ESM samples were performed and demonstrated suitable profiles for translational applications. Biological in vitro studies using SV40 immortalised corneal epithelial cells (ihCEC) and corneal mesenchymal stromal cells (C-MSC) demonstrated excellent biocompatibility. Taken together, these results document the development of a novel sustainable biomaterial that may be used for ophthalmic wounds and/or other biomedical therapies.

The Influence of Oily Vehicle Composition and Vehicle-Membrane Interactions on the Diffusion of Model Permeants across Barrier Membranes.

In many instances, one or more components of a pharmaceutical or cosmetic formulation is an oil. The aims of this study were two-fold. First, to examine the potential of preferential uptake of one oily vehicle component over another into a model barrier membrane (silicone) from blended vehicles (comprising two from the common excipients isohexadecane (IHD), hexadecane (HD), isopropyl myristate (IPM), oleic acid (OA) and liquid paraffin). Second, to study the effect of membrane-vehicle interactions on the diffusion of model permeants (caffeine (CF), methyl paraben (MP) and butyl paraben (BP)) from blended vehicles. Selective sorption and partition of some oils (especially IHD and IPM) at the expense of other oils (such as OA) was demonstrated to take place. For example, the membrane composition of IHD was enriched compared to a donor solution of IHD-OA: 41%, 63% and 82% IHD, compared to donor solution composition of 25%, 50% and 75% IHD, respectively. Pre-soaking the membrane in IHD, HD or LP, rather than phosphate buffer, enhanced the flux of MP through the membrane by 2.6, 1.7 and 1.3 times, respectively. The preferential sorption of individual oil components from mixtures altered the barrier properties of silicone membrane, and enhanced the permeation of CF, MP and BP, which are typically co-formulated in topical products.

Multivariate Analytical Approaches to Identify Key Molecular Properties of Vehicles, Permeants and Membranes That Affect Permeation through Membranes.

There has been considerable recent interest in employing computer models to investigate the relationship between the structure of a molecule and its dermal penetration. Molecular permeation across the epidermis has previously been demonstrated to be determined by a number of physicochemical properties, for example, the lipophilicity, molecular weight and hydrogen bonding ability of the permeant. However little attention has been paid to modeling the combined effects of permeant properties in tandem with the properties of vehicles used to deliver those permeants or to whether data obtained using synthetic membranes can be correlated with those obtained using human epidermis. This work uses Principal Components Analysis (PCA) to demonstrate that, for studies of the diffusion of three model permeants (caffeine, methyl paraben and butyl paraben) through synthetic membranes, it is the properties of the oily vehicle in which they are applied that dominated the rates of permeation and flux. Simple robust and predictive descriptor-based quantitative structure-permeability relationship (QSPR) models have been developed to support these findings by utilizing physicochemical descriptors of the oily vehicles to quantify the differences in flux and permeation of the model compounds. Interestingly, PCA showed that, for the flux of co-applied model permeants through human epidermis, the permeation of the model permeants was better described by a balance between the physicochemical properties of the vehicle and the permeant rather than being dominated solely by the vehicle properties as in the case of synthetic model membranes. The important influence of permeant solubility in the vehicle along with the solvent uptake on overall permeant diffusion into the membrane was substantiated. These results confirm that care must be taken in interpreting permeation data when synthetic membranes are employed as surrogates for human epidermis; they also demonstrate the importance of considering not only the permeant properties but also those of both vehicle and membrane when arriving at any conclusions relating to permeation data.

Discovery of novel small molecule inhibitors of S100P with in vitro anti-metastatic effects on pancreatic cancer cells.

S100P, a calcium-binding protein, is known to advance tumor progression and metastasis in pancreatic and several other cancers. Herein is described the in silico identification of a putative binding pocket of S100P to identify, synthesize and evaluate novel small molecules with the potential to selectively bind S100P and inhibit its activation of cell survival and metastatic pathways. The virtual screening of a drug-like database against the S100P model led to the identification of over 100 clusters of diverse scaffolds. A representative test set identified a number of structurally unrelated hits that inhibit S100P-RAGE interaction, measured by ELISA, and reduce in vitro cell invasion selectively in S100P-expressing pancreatic cancer cells at 10 µM. This study establishes a proof of concept in the potential for rational design of small molecule S100P inhibitors for drug candidate development.

The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions.

The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative platforms. The evolution of computational approaches is driven by the essential criteria that a drug molecule has to fulfill, from the affinity to targets to minimal side effects while having adequate absorption, distribution, metabolism, and excretion (ADME) properties. A combination of ligand- and structure-based drug development approaches is already used to obtain consensus predictions of small molecule activities and their off-target interactions. Further integration of these methods into easy-to-use workflows informed by systems biology could realize the full potential of available data in the drug discovery and reduce the attrition of drug candidates.

Detection of newly emerging psychoactive substances using Raman spectroscopy and chemometrics.

A novel approach for the identification of New Psychoactive Substances (NPS) by means of Raman spectroscopy coupled with Principal Components Analysis (PCA) employing the largest dataset of NPS reference materials to date is reported here. Fifty three NPS were selected as a structurally diverse subset from an original dataset of 478 NPS compounds. The Raman spectral profiles were experimentally acquired for all 53 substances, evaluated using a number of pre-processing techniques, and used to generate a PCA model. The optimum model system used a relatively narrow spectral range (1300-1750 cm-1) and accounted for 37% of the variance in the dataset using the first three principal components, despite the large structural diversity inherent in the NPS subset. Nonetheless, structurally similar NPS (i.e., the synthetic cannabinoids FDU-PB-22 & NM-2201) grouped together in the PCA model based on their Raman spectral profiles, while NPS with different chemical scaffolds (i.e., the benzodiazepine flubromazolam and the cathinone α-PBT) were well delineated, occupying markedly different areas of the three-dimensional scores plot. Classification of NPS based on their Raman spectra (i.e., chemical scaffolds) using the PCA model was further investigated. NPS that were present in the initial dataset of 478 NPS but were not part of the selected 53 training set (validation set) were observed to be closely aligned to structurally similar NPS within the generated model system in all cases. Furthermore, NPS that were not present in the original dataset of 478 NPS (test set) were also shown to group as expected in the model (i.e., methamphetamine and N-ethylamphetamine). This indicates that, for the first time, a model system can be applied to potential 'unknown' psychoactive substances, which are new to the market and absent from existing chemical libraries, to identify key structural features to make a preliminary classification. Consequently, it is anticipated that this study will be of interest to the broad scientific audience working with large structurally diverse chemical datasets and particularly to law enforcement agencies and associated scientific analytical bodies worldwide investigating the development of novel identification methodologies for psychoactive substances.

New inhibitor targeting human transcription factor HSF1: effects on the heat shock response and tumor cell survival.

Comparative modeling of the DNA-binding domain of human HSF1 facilitated the prediction of possible binding pockets for small molecules and definition of corresponding pharmacophores. In silico screening of a large library of lead-like compounds identified a set of compounds that satisfied the pharmacophoric criteria, a selection of which compounds was purchased to populate a biased sublibrary. A discriminating cell-based screening assay identified compound 001, which was subjected to systematic analysis of structure-activity relationships, resulting in the development of compound 115 (IHSF115). IHSF115 bound to an isolated HSF1 DNA-binding domain fragment. The compound did not affect heat-induced oligomerization, nuclear localization and specific DNA binding but inhibited the transcriptional activity of human HSF1, interfering with the assembly of ATF1-containing transcription complexes. IHSF115 was employed to probe the human heat shock response at the transcriptome level. In contrast to earlier studies of differential regulation in HSF1-naïve and -depleted cells, our results suggest that a large majority of heat-induced genes is positively regulated by HSF1. That IHSF115 effectively countermanded repression in a significant fraction of heat-repressed genes suggests that repression of these genes is mediated by transcriptionally active HSF1. IHSF115 is cytotoxic for a variety of human cancer cell lines, multiple myeloma lines consistently exhibiting high sensitivity.

Predicting the Fine Particle Fraction of Dry Powder Inhalers Using Artificial Neural Networks.

Dry powder inhalers are increasingly popular for delivering drugs to the lungs for the treatment of respiratory diseases, but are complex products with multivariate performance determinants. Heuristic product development guided by in vitro aerosol performance testing is a costly and time-consuming process. This study investigated the feasibility of using artificial neural networks (ANNs) to predict fine particle fraction (FPF) based on formulation device variables. Thirty-one ANN architectures were evaluated for their ability to predict experimentally determined FPF for a self-consistent dataset containing salmeterol xinafoate and salbutamol sulfate dry powder inhalers (237 experimental observations). Principal component analysis was used to identify inputs that significantly affected FPF. Orthogonal arrays (OAs) were used to design ANN architectures, optimized using the Taguchi method. The primary OA ANN r2 values ranged between 0.46 and 0.90 and the secondary OA increased the r2 values (0.53-0.93). The optimum ANN (9-4-1 architecture, average r2 0.92 ± 0.02) included active pharmaceutical ingredient, formulation, and device inputs identified by principal component analysis, which reflected the recognized importance and interdependency of these factors for orally inhaled product performance. The Taguchi method was effective at identifying successful architecture with the potential for development as a useful generic inhaler ANN model, although this would require much larger datasets and more variable inputs.

Small molecule recognition of mephedrone using an anthracene molecular clip.

An anthracene molecular probe has been synthesised and shown to target mephedrone, a stimulant drug from the cathinone class of new psychoactive substances (NPS). A protocol has been developed to detect mephedrone via the probe using NMR spectroscopy in a simulated street sample containing two of the most common cutting agents, benzocaine and caffeine.

Elemental fingerprinting of Hypericum perforatum (St John's Wort) herb and preparations using ICP-OES and chemometrics.

St. John's wort (SJW) (Hypericum perforatum) is a herbal remedy commonly used to treat mild depression. The elemental profiles of 54 samples (i.e., dry herbs, tablets and capsules) were evaluated by monitoring 25 elements using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). The major elemental constituents in the SJW samples were Ca (300-199,000µg/g), Mg (410-3,530µg/g), Al (4.4-900µg/g), Fe (1.154-760µg/g), Mn (2.4-261µg/g), Sr (0.88-83.6µg/g), and Zn (7-64µg/g). For the sixteen elements that could be reliably quantified, principal component analysis (PCA) was used to investigate underlying patterns in the data. PCA models identified 7 key elements (i.e., Ba, Ca, Cd, Mg, Mo, Ni and Y), which described 85% of the variance in the dataset in the first three principal components. The PCA approach resulted in a general delineation between the three different formulations and provides a basis for monitoring product quality in this manner.

Establishing the importance of oil-membrane interactions on the transmembrane diffusion of physicochemically diverse compounds.

The diffusion process through a non-porous barrier membrane depends on the properties of the drug, vehicle and membrane. The aim of the current study was to investigate whether a series of oily vehicles might have the potential to interact to varying degrees with synthetic membranes and to determine whether any such interaction might affect the permeation of co-formulated permeants: methylparaben (MP); butylparaben (BP) or caffeine (CF). The oils (isopropyl myristate (IPM), isohexadecane (IHD), hexadecane (HD), oleic acid (OA) and liquid paraffin (LP)) and membranes (silicone, high density polyethylene and polyurethane) employed in the study were selected such that they displayed a range of different structural, and physicochemical properties. Diffusion studies showed that many of the vehicles were not inert and did interact with the membranes resulting in a modification of the permeants' flux when corrected for membrane thickness (e.g. normalized flux of MP increased from 1.25±0.13µgcm(-1)h(-1) in LP to 17.94±0.25µgcm(-1)h(-1)in IPM). The oils were sorbed differently to membranes (range of weight gain: 2.2±0.2% for polyurethane with LP to 105.6±1.1% for silicone with IHD). Membrane interaction was apparently dependent upon the physicochemical properties including; size, shape, flexibility and the Hansen solubility parameter values of both the membranes and oils. Sorbed oils resulted in modified permeant diffusion through the membranes. No simple correlation was found to exist between the Hansen solubility parameters of the oils or swelling of the membrane and the normalized fluxes of the three compounds investigated. More sophisticated modelling would appear to be required to delineate and quantify the key molecular parameters of membrane, permeant and vehicle compatibility and their interactions of relevance to membrane permeation.

In silico prediction of aqueous solubility using simple QSPR models: the importance of phenol and phenol-like moieties.

Recently the authors published a robust QSPR model of aqueous solubility which exploited the computationally derived molecular descriptor topographical polar surface area (TPSA) alongside experimentally determined melting point and logP. This model (the "TPSA model") is able to accurately predict to within ± one log unit the aqueous solubility of 87% of the compounds in a chemically diverse data set of 1265 molecules. This is comparable to results achieved for established models of aqueous solubility e.g. ESOL (79%) and the General Solubility Equation (81%). Hierarchical clustering of this data set according to chemical similarity shows that a significant number of molecules with phenolic and/or phenol-like moieties are poorly predicted by these equations. Modification of the TPSA model to additionally incorporate a descriptor pertaining to a simple count of phenol and phenol-like moieties improves the predictive ability within ± one log unit to 89% for the full data set (1265 compounds -8.48 < logS < 1.58) and 82% for a reduced data set (1160 compounds 6.00 < logS < 0.00) which excludes compounds at the sparsely populated extremities of the data range. This improvement can be rationalized as the additional descriptor in the model acting as a correction factor which acknowledges the effect of phenolic substituents on the electronic characteristics of aromatic molecules i.e. the generally positive contribution to aqueous solubility made by phenolic moieties.

Revisiting the general solubility equation: in silico prediction of aqueous solubility incorporating the effect of topographical polar surface area.

The General Solubility Equation (GSE) is a QSPR model based on the melting point and log P of a chemical substance. It is used to predict the aqueous solubility of nonionizable chemical compounds. However, its reliance on experimentally derived descriptors, particularly melting point, limits its applicability to virtual compounds. The studies presented show that the GSE is able to predict, to within 1 log unit, the experimental aqueous solubility (log S) for 81% of the compounds in a data set of 1265 diverse chemical structures (-8.48 < log S < 1.58). However, the predictive ability of the GSE is reduced to 75% when applied to a subset of the data (1160 compounds -6.00 < log S < 0.00), which discounts those compounds occupying the sparsely populated regions of data space. This highlights how sparsely populated extremities of data sets can significantly skew results for linear regression-based models. Replacing the melting point descriptor of the GSE with a descriptor which accounts for topographical polar surface area (TPSA) produces a model of comparable quality to the GSE (the solubility of 81% of compounds in the full data set predicted accurately). As such, we propose an alternative simple model for predicting aqueous solubility which replaces the melting point descriptor of the GSE with TPSA and hence can be applied to virtual compounds. In addition, incorporating TPSA into the GSE in addition to log P and melting point gives a three descriptor model that improves accurate prediction of aqueous solubility over the GSE by 5.1% for the full and 6.6% for the reduced data set, respectively.

De novo ligand design to partially flexible active sites: application of the ReFlex algorithm to carboxypeptidase A, acetylcholinesterase, and the estrogen receptor.

Reflex is a recent algorithm in the de novo ligand design software, SkelGen, that allows the flexibility of amino acid side chains in a protein to be taken into account during the drug-design process. In this paper the impact of flexibility on the solutions generated by the de novo design algorithm, when applied to carboxypeptidase A, acetylcholinesterase, and the estrogen receptor (ER), is investigated. The results for each of the targets indicate that when allowing side-chain movement in the active site, solutions are generated that were not accessible from the multiple static protein conformations available for these targets. Furthermore, an analysis of structures generated in a flexible versus a static ER active site suggests that these additional solutions are not merely noise but contain many interesting chemotypes.

SkelGen: a general tool for structure-based de novo ligand design.

The recent lapse in productivity in the pharmaceutical industry has facilitated the emergence of experimental and in silico structure-based design methodologies, based on identification of biologically active low molecular weight fragments that can be exploited to produce potential drug candidates with diverse chemistries. SkelGen, an in silico example of this methodology, is reviewed. The ability of this algorithm to identify chemically diverse low molecular weight fragments that would potentially bind to DNA gyrase is recounted, as is the first purely de novo structure-based design of five compounds that show at least micromolar activity against the estrogen receptor. The ability of the algorithm to incorporate partial protein flexibility during its design of compounds to the estrogen receptor is discussed, and an opinion as to the near and long-term futures for de novo design algorithms is expressed.

Prediction of binding modes for ligands in the cytochromes P450 and other heme-containing proteins.

The cytochromes P450 (P450s) are a family of heme-containing monooxygenase enzymes involved in a variety of functions, including the metabolism of endogenous and exogenous substances in the human body. During lead optimization, and in drug development, many potential drug candidates are rejected because of the affinity they display for drug-metabolising P450s. Recently, crystal structures of human enzymes involved in drug metabolism have been determined, significantly augmenting the prospect of using structure-based design to modulate the binding and metabolizing properties of compounds against P450 proteins. An important step in the application of structure-based metabolic optimization is the accurate prediction of docking modes in heme binding proteins. In this paper we assess the performance of the docking program GOLD at predicting the binding mode of 45 heme-containing complexes. We achieved success rates of 64% and 57% for Chemscore and Goldscore respectively; these success rates are significantly lower than the value of 79% observed with both scoring functions for the full GOLD validation set. Re-parameterization of metal-acceptor interactions and lipophilicity of planar nitrogen atoms in the scoring functions resulted in a significant increase in the percentage of successful dockings against the heme binding proteins (Chemscore 73%, Goldscore 65%). The modified scoring functions will be useful in docking applications on P450 enzymes and other heme binding proteins.