COSMO-RS blind prediction of distribution coefficients and aqueous pKa values from the SAMPL8 challenge.

The SAMPL8 blind prediction challenge, which addresses the acid/base dissociation constants (pKa) and the distribution coefficients (logD), was addressed by the Conductor like Screening Model for Realistic Solvation (COSMO-RS). Using the COSMOtherm implementation of COSMO-RS together with a rigorous conformational sampling, yielded logD predictions with a root mean square deviation (RMSD) of 1.36 log units over all 11 compounds and seven bi-phasic systems of the data set, which was the most accurate of all contest submissions (logD).For the SAMPL8 pKa competition, participants were asked to report the standard state free energies of all microstates, which were then used to calculate the macroscopic pKa. We have used COSMO-RS based linear free energy fit models to calculate the requested energies. The assignment of the calculated and experimental pKa values was made on the basis of the popular transitions, i.e. the transition hat was predicted by the majority of the submissions. With this assignment and a model that covers both, pKa and base pKa, we achieved an RMSD of 3.44 log units (18 pKa values of 14 molecules), which is the second place of the six ranked submissions. By changing to an assignment that is based on the experimental transition curves, the RMSD reduces to 1.65. In addition to the ranked contribution, we submitted two more data sets, one for the standard pKa model and one or the standard base pKa model of COSMOtherm. Using the experiment based assignment with the predictions of the two sets we received a RMSD of 1.42 log units (25 pKa values of 20 molecules). The deviation mainly arises from a single outlier compound, the omission of which leads to an RMSD of 0.89 log units.

Prediction of the n-Octanol/Water Partition Coefficients of Basic Compounds Using Multi-Parameter QSRR Models Based on IS-RPLC Retention Behavior in a Wide pH Range.

The n-octanol-water partition coefficient (logP) is an important physicochemical parameter which describes the behavior of organic compounds. In this work, the apparent n-octanol/water partition coefficients (logD) of basic compounds were determined using ion-suppression reversed-phase liquid chromatography (IS-RPLC) on a silica-based C18 column. The quantitative structure-retention relationship (QSRR) models between logD and logkw (logarithm of retention factor corresponding to 100% aqueous fraction of mobile phase) were established at pH 7.0-10.0. It was found that logD had a poor linear correlation with logkw at pH 7.0 and pH 8.0 when strongly ionized compounds were included in the model compounds. However, the linearity of the QSRR model was significantly improved, especially at pH 7.0, when molecular structure parameters such as electrostatic charge ne and hydrogen bonding parameters A and B were introduced. External validation experiments further confirmed that the multi-parameter models could accurately predict the logD value of basic compounds not only under strong alkaline conditions, but also under weak alkaline and even neutral conditions. The logD values of basic sample compounds were predicted based on the multi-parameter QSRR models. Compared with previous work, the findings of this study extended the pH range for the determination of the logD values of basic compounds, providing an optional mild pH for IS-RPLC experiments.

Comparison of logP and logD correction models trained with public and proprietary data sets.

In drug discovery, partition and distribution coefficients, logP and logD for octanol/water, are widely used as metrics of the lipophilicity of molecules, which in turn have a strong influence on the bioactivity and bioavailability of potential drugs. There are a variety of established methods, mostly fragment or atom-based, to calculate logP while logD prediction generally relies on calculated logP and pKa for the estimation of neutral and ionized populations at a given pH. Algorithms such as ClogP have limitations generally leading to systematic errors for chemically related molecules while pKa estimation is generally more difficult due to the interplay of electronic, inductive and conjugation effects for ionizable moieties. We propose an integrated machine learning QSAR modeling approach to predict logD by training the model with experimental data while using ClogP and pKa predicted by commercial software as model descriptors. By optimizing the loss function for the ClogD calculated by the software, we build a correction model that incorporates both descriptors from the software and available experimental logD data. Additionally, we calculate logP from the logD model using the software predicted pKa's. Here, we have trained models using publicly or commercial available logD data to show that this approach can improve on commercial software predictions of lipophilicity. When applied to other logD data sets, this approach extends the domain of applicability of logD and logP predictions over commercial software. Performance of these models favorably compare with models built with a larger set of proprietary logD data.

Implementation of QbD Approach to the Development of Chromatographic Methods for the Determination of Complete Impurity Profile of Substance on the Preclinical and Clinical Step of Drug Discovery Studies.

The purpose of this work was to demonstrate the use of the AQbD with the DOE approach to the methodical step-by-step development of a UHPLC method for the quantitative determination of the impurity profile of new CPL409116 substance (JAK/ROCK inhibitor) on the preclinical and clinical step of drug discovery studies. The critical method parameters (CMPs) have been tested extensively: the kind of stationary phase (8 different columns), pH of the aqueous mobile phase (2.6, 3.2, 4.0, 6.8), and start (20-25%) and stop (85-90%) percentage of organic mobile phase (ACN). The critical method attributes (CMAs) are the resolution between the peaks (≥2.0) and peak symmetry of analytes (≥0.8 and ≤1.8). In the screening step, the effects of different levels of CMPs on the CMAs were evaluated based on a full fractional design 22. The robustness tests were established from the knowledge space of the screening step and performed by application fractional factorial design 2(4-1). Method operable design region (MODR) was generated. The probability of meeting the specifications for the CMAs was calculated by Monte-Carlo simulations. In relation to literature such a complete AQbD approach including screening, optimization, and validation steps for the development of a new method for the quantitative determination of the full profile of nine impurities of an innovative pharmaceutical substance with the structure-based pre-development pointed out the novelty of our work. The final working conditions were as follows: column Zorbax Eclipse Plus C18, aqueous mobile phase 10 mM ± 1 mM aqueous solution of HCOOH, pH 2.6, 20% ± 1% of ACN at the start and 85% ± 1% of ACN at the end of the gradient, and column temperature 30 °C ± 2 °C. The method was validated in compliance with ICH guideline Q2(R1). The optimized method is specified, linear, precise, and robust. LOQ is on the reporting threshold level of 0.05% and LOD at 0.02% for all impurities.

Accurate Physical Property Predictions via Deep Learning.

Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory accuracy even further. Here, we proposed a deep-learning architecture model, namely Bidirectional long short-term memory with Channel and Spatial Attention network (BCSA), of which the training process is fully data-driven and end to end. It is based on data augmentation and SMILES tokenization technology without relying on auxiliary knowledge, such as complex spatial structure. In addition, our model takes the advantages of the long- and short-term memory network (LSTM) in sequence processing. The embedded channel and spatial attention modules in turn specifically identify the prime factors in the SMILES sequence for predicting properties. The model was further improved by Bayesian optimization. In this work, we demonstrate that the trained BSCA model is capable of predicting aqueous solubility. Furthermore, our proposed method shows noticeable superiorities and competitiveness in predicting oil-water partition coefficient, when compared with state-of-the-art graphs models, including graph convoluted network (GCN), message-passing neural network (MPNN), and AttentiveFP.

Discovery of 1-[2-(1-methyl-1H-pyrazol-5-yl)-[1,2,4]triazolo[1,5-a]pyridin-6-yl]-3-(pyridin-4-ylmethyl)urea as a potent NAMPT (nicotinamide phosphoribosyltransferase) activator with attenuated CYP inhibition.

Nicotinamide phosphoribosyltransferase (NAMPT) catalyzes the rate-limiting step of the NAD+ salvage pathway. Since NAD+ plays a pivotal role in many biological processes including metabolism and aging, activation of NAMPT is an attractive therapeutic target for treatment of diverse array of diseases. Herein, we report the continued optimization of novel urea-containing derivatives which were identified as potent NAMPT activators. Early optimization of HTS hits afforded compound 12, with a triazolopyridine core, as a lead compound. CYP direct inhibition (DI) was identified as an issue of concern, and was resolved through modulation of lipophilicity to culminate in 1-[2-(1-methyl-1H-pyrazol-5-yl)-[1,2,4]triazolo[1,5-a]pyridin-6-yl]-3-(pyridin-4-ylmethyl)urea (21), which showed potent NAMPT activity accompanied with attenuated CYP DI towards multiple CYP isoforms.

Development of liposome/water partition coefficients predictive models for neutral and ionogenic organic chemicals.

Membrane/water partition coefficient (Km/w) is a vital parameter used to characterize the membrane permeability of compounds. Considering the Km/w value is difficult to observe experimentally for real biological membranes, liposome/water partition coefficient (Klip/w) is employed to approximate Km/w. Here, quantitative structure property relationship (QSPR) models for logKlip/w of the neutral organic chemicals and the neutral form of ionogenic organic chemicals (IOCs) (logKlip/w-neutral), ionic form of IOCs (logKlip/w-ionic), the speciation-corrected liposome-water distribution ratios at a pH = 7.40 (logDlip/w-(pH=7.40)) were developed. In the modeling, two modeling methods (multiple linear regressions (MLR) and k-nearest neighbor (kNN)) were used. The predictive variables employed here could be calculated from the molecular structure directly. For logKlip/w-neutral and logDlip/w-(pH=7.40), the logKOW and logDOW-based, non-logKOW and non-logDOW-based kNN-QSPR and MLR-QSPR models were developed, respectively. The evaluation results implied that the predictive performance of kNN-QSPR models is better than that of MLR-QSPR models. For logKlip/w-ionic, only one acceptable MLR-QSPR model was developed for cation and anion, respectively. The model quality of the derived models was evaluated following the OECD QSPR models validation guideline. The determination coefficient (R2), leave-one-out cross validation Q2 (Q2LOO) and bootstrapping coefficient (Q2BOOT), the external validation coefficient (Q2EXT) of all the models met the acceptable criteria (Q2 > 0.600, R2 > 0.700); while the root-mean-square error (RMSE) range from 0.351 to 0.857. All the results implied that the models had good goodness-of-fit, robustness and predictive ability. Therefore, the developed models could be used to fill the data gap for substances within the applicability domain on their missing logKlip/w-neutral, logKlip/w-ionic, logDlip/w-(pH=7.40) values.

Discovery of polar spirocyclic orally bioavailable urea inhibitors of soluble epoxide hydrolase.

Spirocyclic 1-oxa-9-azaspiro[5.5]undecan-4-amine scaffold was explored as a basis for the design of potential inhibitors of soluble epoxide hydrolase (sEH). Synthesis and testing of the initial SAR-probing library followed by biochemical testing against sEH allowed nominating a racemic lead compound (±)-22. The latter showed remarkable (> 0.5 mM) solubility in aqueous phosphate buffer solution, unusually low (for sEH inhibitors) lipophilicity as confirmed by experimentally determined logD7.4 of 0.99, and an excellent oral bioavailability in mice (as well as other pharmacokinetic characteristics). Individual enantiomer profiling revealed that the inhibitory potency primarily resided with the dextrorotatory eutomer (+)-22 (IC50 4.99 ±â€¯0.18 nM). For the latter, a crystal structure of its complex with a C-terminal domain of sEH was obtained and resolved. These data fully validate (+)-22 as a new non-racemic advanced lead compound for further development as a potential therapeutic agent for use in such areas as cardiovascular disease, inflammation and pain.

Development of a simple proton nuclear magnetic resonance-based procedure to estimate the approximate distribution coefficient at physiological pH (logD7.4): Evaluation and comparison to existing practices.

In drug discovery, lipophilicity is a key parameter for drug optimization. Lipophilicity determinations can be both work and time consuming, especially for non-UV active compounds. Herein, an improved and simple 1H NMR-based method is described to estimate the lipophilicity at physiological pH (logD7.4) in 1-octanol and D2O buffer. The method can be applied to both UV and non-UV active compounds. In addition, neither calibration curves nor internal/external standards are needed. We have demonstrated that logD7.4 can be accurately measured using 1H NMR for compounds within the logD7.4 interval between 0.7 and 3.3. The method was also compared to a previously described HPLC method.

A multi-endpoint matched molecular pair (MMP) analysis of 6-membered heterocycles.

Aromatic rings, ubiquitous in pharmaceutical compounds, are often exchanged with another ring during the optimization process of drug discovery. Inevitably, the preferred ring system for one endpoint may prove detrimental to another, thus necessitating a holistic, multiple endpoint optimization approach for finding the ideal replacement. Accordingly, we conducted an extensive matched molecular pair (MMP) analysis of common 6-membered aromatic rings across 4 endpoints critical for drug discovery (logD lipophilicity, microsomal metabolism, P-gp efflux and passive permeability). We also investigated the effect of context by considering the connecting atom. Heat maps were created as a simple yet comprehensive way to view and analyze the vast amount of interrelated data. Paired difference statistical tests were used to identify transforms with changes that were significantly different from zero. We conclude that the heat maps of transforms provide a unique and powerful approach for multiparameter optimization.

Estimation of Drug Binding to Brain Tissue: Methodology and in Vivo Application of a Distribution Assay in Brain Polar Lipids.

The unbound drug concentration-effect relationship in brain is a key aspect in CNS drug discovery and development. In this work, we describe an in vitro high-throughput distribution assay between an aqueous buffer and a microemulsion of porcine brain polar lipids (BPL). The derived distribution coefficient LogDBPL was applied to the prediction of unbound drug concentrations in brain (Cu,b) and nonspecific binding to brain tissue. The in vivo relevance of the new assay was assessed for a large set of proprietary drug candidates and CNS drugs by (1) comparing observed compound concentrations in rat CSF with Cu,b calculated using the LogDBPL assay in combination with total drug brain concentrations, (2) comparing Cu,b derived from LogDBPL and total drug brain concentrations to Cu,b estimated using in vitro P-glycoprotein efflux ratio data and unbound drug plasma levels, and (3) comparing tissue nonspecific binding data from human brain autoradiography studies for 17 PET tracer candidates to distribution in BPL. In summary, the LogDBPL assay provides a predicted drug fraction unbound in brain tissue that is nearly identical to brain homogenate equilibrium dialysis with an estimation of in vivo Cu,b that is superior to LogD in octanol. LogDBPL complements the approach for predicting Cu,b based on in vitro P-glycoprotein efflux ratio and in vivo unbound plasma concentration and stands as a fast and cost-effective tool for nonspecific brain binding optimization of PET ligand candidates.

Effect of water solvation on the lipophilicity of isomeric pyrimidine-carboxamides.

Incorporation of nitrogen is a common medicinal chemistry tactic to reduce logD values. Neighboring group participation influences logD, so the results are isomer dependent. The logD and logP differences observed between isomeric pyrimidines 1, 2 and 3 presumably result when the carbonyl or ether lone pairs are in close proximity to a heterocyclic nitrogen lone pair, recruiting water to bridge between the electron rich atoms. Various lipophilicity calculators did not discriminate between 1 (logD=2.6) and 3 (logD=1.0), but solvation energies using Poisson-Boltzmann and 3D-RISM methods rationalize the observed differences in lipophilicity among pyrimidine carboxamide isomers.

Salting-out assisted liquid-liquid extraction for the simple and rapid determination of veterinary antibiotic residues in aquatic products by HPLC-MS/MS.

Based on salting-out assisted liquid-liquid extraction (SALLE) and high performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS), a simple, rapid pretreatment without complex clean-up for the determination of 22 veterinary drug residues in aquatic products was developed and validated. In order to improve the efficiency of the method, the key procedural parameters of SALLE were fabricated. Na2EDTA-Mcllvaine buffer/ACN was used as the extraction solvent, anhydrous MgSO4 and NaCl as the extraction salts. The relationship between extraction efficiency and logD was initially evaluated during the optimization process. This study was well validated in various aquatic samples such as bass, large yellow croaker, carp, and shrimp, the limits of detection (LOD) and accuracy for all compounds ranged from 0.5 to 1.0 µg/kg, 71.4% to 120%. This method has the advantages of rapidity, simplicity, low cost, and high efficiency, and has broad potential for risk monitoring and evaluation of veterinary antibiotics in aquatic products.

Toxicokinetics of a developmental toxicity test in zebrafish embryos and larvae: Relationship with drug exposure in humans and other mammals.

To study the effects of drugs on embryo/fetal development (EFD), developmental and reproductive toxicity studies in zebrafish (Danio rerio) embryos is expected to be an accepted alternative method to animal studies using mammals. However, there is a lack of clarity in the relationship between the concentration of developmental toxicity agents in whole embryos or larvae (Ce) and that in aqueous solution (Cw), and also between the amount of drug exposure required to cause developmental toxicity in zebrafish embryos or larvae and that required in mammals. Here, we measured Ce for developmental toxicity agents every 24 h starting at 24 h post fertilization (hpf). We found a high correlation (R 2: 0.87-0.96) between log [Ce/Cw] and the n-octanol-water distribution coefficient at pH 7 (logD) of each drug at all time points up to 120 hpf. We used this relationship to estimate the Ce values of the 21 positive-control reference drugs listed in ICH guidelines on reproductive and developmental toxicity studies (ICH S5). We then calculated the area under the Ce-time curve in zebrafish (zAUC) for each drug from the regression equation between log [Ce/Cw] and logD and compared it with the AUC at the no-observed-adverse-effect level in rats and rabbits and at the effective dose in humans described in ICH S5. The log of the calculated zAUC for the 14 drugs identified as positive in the zebrafish developmental toxicity test was relatively highly positively correlated with the log [AUC] for rats, rabbits, and humans. These findings provide important and positive information on the applicability of the zebrafish embryo developmental toxicity test as an alternative method of EFD testing. (267 words).

[18F]Fluspidine-A PET Tracer for Imaging of σ1 Receptors in the Central Nervous System.

σ1 receptors play a crucial role in various neurological and neurodegenerative diseases including pain, psychosis, Alzheimer's disease, and depression. Spirocyclic piperidines represent a promising class of potent σ1 receptor ligands. The relationship between structural modifications and σ1 receptor affinity and selectivity over σ2 receptors led to the 2-fluoroethyl derivative fluspidine (2, Ki = 0.59 nM). Enantiomerically pure (S)-configured fluspidine ((S)-2) was prepared by the enantioselective reduction of the α,ß-unsaturated ester 23 with NaBH4 and the enantiomerically pure co-catalyst (S,S)-24. The pharmacokinetic properties of both fluspidine enantiomers (R)-2 and (S)-2 were analyzed in vitro. Molecular dynamics simulations revealed very similar interactions of both fluspidine enantiomers with the σ1 receptor protein, with a strong ionic interaction between the protonated amino moiety of the piperidine ring and the COO- moiety of glutamate 172. The 18F-labeled radiotracers (S)-[18F]2 and (R)-[18F]2 were synthesized in automated syntheses using a TRACERlab FX FN synthesis module. High radiochemical yields and radiochemical purity were achieved. Radiometabolites were not found in the brains of mice, piglets, and rhesus monkeys. While both enantiomers revealed similar initial brain uptake, the slow washout of (R)-[18F]2 indicated a kind of irreversible binding. In the first clinical trial, (S)-[18F]2 was used to visualize σ1 receptors in the brains of patients with major depressive disorder (MDD). This study revealed an increased density of σ1 receptors in cortico-striato-(para)limbic brain regions of MDD patients. The increased density of σ1 receptors correlated with the severity of the depressive symptoms. In an occupancy study with the PET tracer (S)-[18F]2, the selective binding of pridopidine at σ1 receptors in the brain of healthy volunteers and HD patients was shown.

LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP.

Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios.

High-throughput screening of LogD by using a sample pooling approach based on the traditional shake flask method.

A new approach for screening LogD is presented. The method is based on the shake flask method combined with rapid generic LC-MS/MS bioanalysis by using a sample pooling approach that enables high-throughput screening of LogD or LogP in the drug discovery stage. The method is evaluated by a comparison of measured LogD between single and pooled compounds for a test set of structurally diverse compounds with a wide range of LogD values (from -0.04 to 6.01). Test compounds include 10 commercially available drug standards along with 27 new chemical entities. A good correlation (RMSE = 0.21, R2 = 0.9879) of LogD between the single and pooled compounds was obtained, suggesting that at least 37 compounds can be simultaneously measured with acceptable accuracy. The sample pooling method significantly reduced the number of bioanalysis samples as compared to the single compound measurement by the conventional shake flask method. The impact of DMSO content on LogD measurement was also investigated and the result demonstrated that at least 0.5% DMSO was tolerated in this method. The current new development will facilitate the drug discovery process by more rapidly assessing the LogD or LogP of drug candidates.

A Study of the Mechanism and Separation of Structurally Similar Phenolic Acids by Commercial Polymeric Ultrafiltration Membranes.

This study examined the behavior and penetration mechanisms of typical phenolic (benzoic) acids, which determine their observed penetration rates during membrane separation, focusing on the influence of electrostatic and hydrophobic solute/membrane interactions. To understand the effects of hydrophobicity and electrostatic interaction on membrane filtration, the observed penetration of five structurally similar phenolic acids was compared with regenerated cellulose (RC) and polyamide (PA) membranes at different solute concentrations and solution pHs. Variation partitioning analysis (VPA) was performed to calculate the relative contributions of electrostatic and hydrophobic effects. The penetration of phenolic acids was mainly influenced by the electrostatic interaction, with salicylic acid having the highest penetration. Penetration of phenolic acids through the PA membrane decreased from 98% at pH 3.0 to 30-50% at pH 7.4, indicating the dominance of the electrostatic interaction. Moreover, based on its hydrophobicity and greater surface charge, the PA membrane could separate binary mixtures of protocatechuic/salicylic acid and 4-hydroxybenzoic/salicylic acid at pH 9.0, with separation factors of 1.81 and 1.78, respectively. These results provide a greater understanding of solute/membrane interactions and their effect on the penetration of phenolic acids through polymeric ultrafiltration membranes.

Modulating physicochemical properties of tetrahydropyridine-2-amine BACE1 inhibitors with electron-withdrawing groups: A systematic study.

A common challenge for medicinal chemists is to reduce the pKa of strongly basic groups' conjugate acids into a range that preserves the desired effects, usually potency and/or solubility, but avoids undesired effects like high volume of distribution (Vd), limited membrane permeation, and off-target binding to, notably, the hERG channel and monoamine receptors. We faced this challenge with a 3,4,5,6-tetrahydropyridine-2-amine scaffold harboring an amidine, a key structural component of potential inhibitors of BACE1, the rate-limiting enzyme in the production of Aß species that make up amyloid plaques in Alzheimer's disease. In our endeavor to balance potency with desirable properties to achieve brain penetration, we introduced a diverse set of groups in beta position of the amidine that modulate logD, PSA and pKa. Given the synthetic challenge to prepare these highly functionalized warheads, we first developed a design flow including predicted physicochemical parameters which allowed us to select only the most promising candidates for synthesis. For this we evaluated a set of commercial packages to predict physicochemical properties, which can guide medicinal chemists in their endeavors to modulate pKa values of amidine and amine bases.

Synthesis of Aminoethyl-Substituted Piperidine Derivatives as σ1 Receptor Ligands with Antiproliferative Properties.

A series of novel σ1 receptor ligands with a 4-(2-aminoethyl)piperidine scaffold was prepared and biologically evaluated. The underlying concept of our project was the improvement of the lipophilic ligand efficiency of previously synthesized potent σ1 ligands. The key steps of the synthesis comprise the conjugate addition of phenylboronic acid at dihydropyridin-4(1H)-ones 7, homologation of the ketones 8 and introduction of diverse amino moieties and piperidine N-substituents. 1-Methylpiperidines showed particular high σ1 receptor affinity and selectivity over the σ2 subtype, whilst piperidines with a proton, a tosyl moiety or an ethyl moiety exhibited considerably lower σ1 affinity. Molecular dynamics simulations with per-residue binding free energy deconvolution demonstrated that different interactions of the basic piperidine-N-atom and its substituents (or the cyclohexane ring) with the lipophilic binding pocket consisting of Leu105, Thr181, Leu182, Ala185, Leu186, Thr202 and Tyr206 are responsible for the different σ1 receptor affinities. Recorded logD7.4 and calculated clogP values of 4a and 18a indicate low lipophilicity and thus high lipophilic ligand efficiency. Piperidine 4a inhibited the growth of human non-small cell lung cancer cells A427 to a similar extent as the σ1 antagonist haloperidol. 1-Methylpiperidines 20a, 21a and 22a showed stronger antiproliferative effects on androgen negative human prostate cancer cells DU145 than the σ1 ligands NE100 and S1RA.