MedChemica Logo

Tel: +44 (0)1625 238843

MedChemica Logo
MedChemica Logo
Bucket List2021-10-06T11:40:31+00:00

MedChemica Bucket List

Accelerating the life sciences ecosystem

The MedChemica Bucket List.

The MedChemica Bucket List

BucketListPapers 75/100: Starting from the natural substrate as a drug hunting lead : a modern example.

brillinta SAR

Before the days of high through screening, starting from the natural substrate was the method of generating chemical leads for drug discovery. Beginning with ATP, a quite poor lead for an oral programme, a potent P2Y12 antagonists with found. Remarkably all the elements in the medicinal chemical journey have been crammed into this BMCL paper! Of note is the replacement of the labile triphosphate group and increasing affinity with a triazolopyrimidine core and introducing a trans-2-phenylcyclopropylamino substituent. The resultant drug Ticagrelor is quite complex and the manufacturing route is whole other story too.

Ticagrelor.

From ATP to AZD6140: the discovery of an orally active reversible P2Y12 receptor antagonist for the prevention of thrombosis.

Bioorg. & Med. Chem. Lett. 2007, 17, 6013-6018

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-11-22T14:05:11+00:00

BucketListPapers 74/100: Unusual chemical groups can make it into drugs.

Vaborbactam design idea

Carbapenem-resistant Enterobacteriaceae (CRE), due to the Klebsiella pneumoniae carbapenemase (KPC) and other β-lactamases, now threatens the usefulness of all β-lactam antibiotics. Boronic acids offered an intriguing group to mimic the transition state of the lactamases and were already known. The research took a highly modern approach of modelling possibilities and generated a new series with a novel cyclic boronic acid. In this manner very few compounds were actually made and profiled – a great example of a highly rationale approach and modern molecular modelling.

Vaborbactam – Discovery of a Cyclic Boronic Acid β-Lactamase Inhibitor (RPX7009) with Utility vs Class A Serine Carbapenemases.

J. Med. Chem. 2015, 58, 9, 3682–3692

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-11-18T14:05:04+00:00

BucketListPapers 73/100: Amide isosteres and fluorines in the right place.

sitagliptin xtal

The discovery of Sitagliptin is a story of finding an amide isostere that worked, then optimisation of pharmacokinetics via some fluorines in the right place. Metabolically labile amides can be replaced 5 membered heterocycles, where a lone pair of an aromatic N mimics the H-bonding of an amide carbonyl. Here the scientists at Pfizer combined this by “fusing” the five membered ring into a piperazine to generate a novel heterocycle. The rest of the optimisation was finding a CF3 group was optimal for bio-availability, although the reason for this is not clear. Interestingly, a pattern of fluorine atoms on the end benzene ring enhanced binding, and subsequent x-ray structure determination of the molecule bound to protein showed how good the fit was.

SITAGLIPTIN[MK-0431] (2R)-4-Oxo-4-[3-(Trifluoromethyl)-5,6-dihydro[1,2,4]triazolo[4,3-a]pyrazin- 7(8H)-yl]-1-(2,4,5-trifluorophenyl)butan-2-amine:  A Potent, Orally Active Dipeptidyl Peptidase IV Inhibitor for the Treatment of Type 2 Diabetes

J. Med. Chem. 2005, 48, 1, 141–151

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-11-15T14:05:05+00:00

BucketListPapers 72/100: Improving pharmacokinetics and reactive metabolites.

Suvorexant discovery

Lead optimisation frequently includes involves work to improve bio-availability, lower clearance and improve solubility. In addition, early safety studies take place, and usually this is the first time more details emerge of the fate of leading compounds in-vivo. The formation of reactive metabolites is undesirable as they could cause drug-induced idiosyncratic toxicity in humans. The work by Merck for a new insomnia drug involved optimisation of PK and elimination reactive metabolites; the solution was a completely different aromatic group, one that overall, yielded a compound that was more lipophilic than the advanced yield.

Suvorexant – Discovery of the Dual Orexin Receptor Antagonist [(7R)-4-(5-Chloro-1,3-benzoxazol-2-yl)-7-methyl-1,4-diazepan-1-yl][5-methyl-2-(2H-1,2,3-triazol-2-yl)phenyl]methanone (MK-4305) for the Treatment of Insomnia.

J. Med. Chem. 2010, 53, 14, 5320–5332.

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-11-11T14:05:53+00:00

BucketListPapers 71/100: Thinking through the chemistry paves the way to success.

Daclatasvir discovery

A triumph of the decade (2010 – 2019) is the suite of Hepatitis C small molecule drugs treatments. These took a previously fatal disease to a 97% cure rate in 6 to 12 weeks of treatment. The second half of the decade saw the medicinal chemists involved presenting their work at major conferences. One of the most impressive of these talks, and it is reflected in the publication, was the discovery of Daclatasvir. By phenotypic screening a relatively unattractive hit was found and the SAR explored. The breakthrough came when the instability of the compound was examined and a dimerization process discovered. An almost complete rework occurred and the diligence of the chemists yielded a candidate drug with exquisite potency and high bio-availability. A remarkable piece of work.

Discovery of Daclatasvir, a Pan-Genotypic Hepatitis C Virus NS5A Replication Complex Inhibitor with Potent Clinical Effect

J. Med. Chem. 2014, 57, 12, 5057–5071

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-11-09T10:45:44+00:00

BucketListPapers 70/100: Overcoming a hERG inhibition problem.

CCR5 hERG problem

The GPCR receptors CCR5 and CCR4 are recognised on the surface of host T-cells by HIV virus particles. It was found that a group of the population had a mutation so their CCR5 receptors were not recognised and so did not get infected. This attractive target was tackled by many organisations and nearly all of them found similar chemical matter in the form of a bi-aryl group linked to a basic nitrogen. As these programs continued they coincided with the requirement for NCE to avoid (or be free of) any binding to the human ether a-go-go related gene product (aka hERG) ion-channel. The Pfizer research program had to optimise against this binding as well as bio-availability and in-vitro and in-vivo reduction of viral load.

Overcoming HERG affinity in the discovery of the CCR5 antagonist maraviroc.

Bioorg. & Med. Chem. Lett. 2006, 16, 4633-4637.

 

Further hERG references:

Medicinal Chemistry of hERG Optimizations:  Highlights and Hang-Ups

J. Med. Chem. 2006, 49, 17, 5029–5046

hERG Me Out

J. Chem. Inf. Model. 2013, 53, 9, 2240–2251

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-11-09T10:45:36+00:00

BucketListPapers 69/100: Raltegravir: Optimising cellular activity due to blood protein binding.

Raltalgvir LO

HIV-Integrase was also discovered as another method of disrupting viral replication in host cells. Merck were working on multiple viral targets and had quality lead compounds, including inhibitors for Hepatitus-C. Combining leads into one series initial appeared unfruitful and the reason for doing this appears unclear. It is usual in anti-infective programs to measure IC95 inhibition, and also at assays containing blood serum, which can suppress the activity as free concentration of active compound is reduced. Without high virus suppression replication continues and the drug has next to no effect. This program is an illustration of a chemical series that was highly bound to blood proteins: an acidic core scaffold and a lipophilic aryl group, being the driver for this. The authors took the approach of looking at another part of the molecule, where polar groups can be added to modulate the blood protein binding. SAR exploration yielded two groups that resulted in candidate drugs.

Discovery of Raltegravir, a Potent, Selective Orally Bioavailable HIV-Integrase Inhibitor for the Treatment of HIV-AIDS Infection.

J. Med. Chem. 2008, 51, 18, 5843–5855

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-11-01T14:05:41+00:00

BucketListPapers 68/100: Indinavir – optimising by structure based design

Indinavir SAR table

It is perhaps not surprising that we have chosen three anti-HIV drug discovery projects. The failure to find a vaccine for HIV, in the second half of the eighties, launched a concerted push to develop small molecule drugs. This was building on the success of the nucleoside reverse transcriptase inhibitors (NRTI) compounds such as AZT. This effort coincided with improved Structure Based Drug Design (SBDD) via co-crystals and 3D modelling, and higher through-put in-vitro assays. The protease of HIV had been discovered and early compounds were shown to reduce virus load in-vitro. Using the transition state isostere concept hydroxyethylene dipeptide isostere inhibitors were designed and known. The story of the discovery of Indinavir starts with their own in-house inhibitors, and an early compound from Roche. These compounds had poor aqueous solubility and no bio-availability, but with 3D modelling the researches saw the opportunity of taking the basic nitrogen groups from the Roche compound into their series and this led to Indinavir.

Indinavir; L-735,524: The Design of a Potent and Orally Bioavailable HIV Protease Inhibitor.

J. Med. Chem. 1994, 37, 21, 3443–3451

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-10-28T14:12:04+00:00

BucketListPapers 67/100: From HTS hit to candiate drug.

Invacaftor discovery

Cystic fibrosis (CF) is a lethal genetic disease that affects approximately 70000 patients worldwide. The story of the discovery of Ivacaftor starts with a specialised high content screening campaign, which yielded a fairly average ‘looking’ hit. This modern discovery effort fully describes the complete work up and optimisation of a hit into a candidate drug – this is quite unusual, as many programs end up breaking down the work into several publications. On note is the full and detailed characterisation of the hit compound (about 2 uM), which put the project in a strong position. At this point a hit with a relatively low molecular weight of 368 and cLogP of 2.9, and well understood functional activity, was very attractive. The initial SAR exploration is excellent: a handful of well thought out compounds showed the binding mode of the series, and which tautomer form was key to binding – take note. The second exploration found a new hydrogen bond to explore and optimise, in the form of an indole. Take note again the team fully characterised this compound, and found excellent selectivity against a panel of protein targets, but poor solubility and sub-optimal pharmacokinetics – this again placed the project in a strong position. Next to understand the poor solubility the team proposed and modelled potential intramolecular hydrogen bonds and a planar structure, which were confirmed by a single crystal x-ray determinations – this is the definitive method. Efforts to disrupt the intramolecular H-bonds and planarity did find tert-butyl groups could be added. This approach would normally yield a molecule high in lipophilicity an unlikely to have good solubility. However, work to find an iso-steres to indole found a phenol group could be used. Normally a classic med-chem change is phenol to indole, but this is the reverse. Phenols are not normally desirable as they undergo secondary metabolism resulting in short half-life, but flanked with a t-butyl group this clearly does not occur as PK was good for this combination of groups.

Ivacaftor – Discovery of N-(2,4-Di-tert-butyl-5-hydroxyphenyl)-4-oxo-1,4-dihydroquinoline-3-carboxamide (VX-770, Ivacaftor), a Potent and Orally Bioavailable CFTR Potentiators.

J. Med. Chem. 2014, 57, 23, 9776–9795.

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-10-25T14:05:57+00:00

BucketListPapers 66/100: The first case study – Celecoxib – introducing a metabolically labile group to control half-life.

Selecting drug discovery case studies for the BucketList

Here starts a set of drug discovery ‘case studies’; selected papers that describes the research that led to an approved drug to market. If we just selected these papers on the basis of being an approved drug, we could probably generate another bucket list. We could also have chosen papers that are excellent examples of the current standard of scientific write ups. However, the most useful papers are those where the research has solved a particular problem, and this is well described, with clear tables of data. We think these papers have the most educational benefit, and serve as a good references. However there is a bias towards some of the more recent ‘drugs to market’, simple because of the standard of the write up, particular those in J.Med.Chem. Lastly, if we came across a paper that has been made open access by the authors, we selected that over others.

We should note some classics from history, as honourable mentions.

Let us mention the first beta-blocker: Propranolol which led to a Nobel prize. Black JW, Crowther AF, Shanks RG, Smith LH, Dornhorst AC (May 1964). “A New Adrenergic”.

Lancet. 1 (7342): 1080–1.

And Cimetidine, considered by many as the ‘first rationally designed’ drug.

Characterization And Development Of Cimetidine As A Histamine Hz-Receptor Antagonist. Gastroenterology 74:339-347, 1978

And the Captoprl story. Science, 1977, 196, 441-444.

On the subject of writing a quality med chem paper – please read:

Writing Your Next Medicinal Chemistry Article: Journal Bibliometrics and Guiding Principles for Industrial Authors

J. Med. Chem. 2020, 63, 14336−14356

 

Case Study : Celecoxib

Celecoxib SAR table

The inhibition of Cyclooxygenase-2 requires a molecule with a five membered ring substituted with a 1,2-diphenyl groups. The series discovered Pfizer was unusually stable, having an un-expectably long half-life in rodent. The solution was to introduce a metabolically labile group (4-Methyl – compound 1i – Table 1). This generally is the reverse of what is required in typical drug hunting projects, with the exception of inhaled drugs a short half-life is often desired. The approach to solving the problems, and late stage in-vivo profiling is well described.

Synthesis and Biological Evaluation of the 1,5-Diarylpyrazole Class of Cyclooxygenase-2 Inhibitors:  Identification of 4-[5-(4-Methylphenyl)-3- (trifluoromethyl)-1H-pyrazol-1-yl]benzenesulfonamide (SC-58635, Celecoxib)

J.Med. Chem. 1997, 40, 9, 1347–1365

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-10-21T14:05:54+00:00

BucketListPapers 65/100: Another invaluable list for your med chem knapsack.

Bad proteins in preclinical

We send this reference out to our clients and contacts more than any other paper. It describes the rationale, strategies and methodologies for in vitro pharmacological profiling at four major pharmaceutical companies (AstraZeneca, GlaxoSmithKline, Novartis and Pfizer), and illustrates, with examples of their impact, on the drug discovery process. For the first time there was a disclosed a list of proteins, that small molecule interaction has been linked to undesired side effects in patients. Such effects led to discontinuation of the drug at obvious high cost. The knowledge of this list, coupled with early in-vitro screening, is a must have for medicinal chemistry.

Reducing safety-related drug attrition: the use of in vitro pharmacological profiling.

Nature reviews Drug Discovery, 2012, 11, 909

2021-10-18T14:05:47+00:00

BucketListPapers 64/100: Lipophilic candidate drugs tend to be discontinued.

Lipophilic and fate in dev

The first of these two papers, examining the physical chemical properties of drugs and the in-vivo and clinical outcomes, caused a stir when first published. This resulted in the one of the authors going ‘on tour’, around the conference circuit, for about a year, as we recall. A simple study comparing the properties of successful phase 1,2 and 3 drugs, against those that were discontinued produced a simple conclusion. Other properties were also examined and the authors subsequently produced more detailed studies that were subsequently only disclosed at conferences. The work led, we believe to the term ‘developability’, and various methods of calculating a score.

The second paper performs a more rigorous statistical study of compound properties and in-vivo outcomes. This work is quite detailed, and quite hard to follow the stats, but importantly the work considers the inter-connectivity of properties.

A Comparison of Physiochemical Property Profiles of Development and Marketed Oral Drugs

J. Med. Chem. 2003, 46, 7, 1250–1256

Relating Molecular Properties and in Vitro Assay Results to in Vivo Drug Disposition and Toxicity Outcomes

J.Med. Chem. 2012, 55, 14, 6455–6466.

2021-10-14T14:05:22+00:00

BucketListPapers 63/100: Relating chemical properties to outcomes in early pre-clinical toxicology studies… and a missing conclusion?

Properties to tox

In early drug development, candidate compounds undergo testing in in-vivo animal safety models. The results of these are usually written up in text documents, and not usually broken up into data points to be loaded onto a database, for example. The drug discovery chemists of this bucket list paper took these “raw” text documents and entered the results into spreadsheets to allow study against the chemical properties of the drug candidates – this itself is pretty heroic. A complication of any analysis involving in-vivo data is the dose / concentration of compound in blood will be different from study to study. For statistical rigour, the group chose the 10uM Total Drug Threshold as there was an even number of “clean” versus “toxic” outcomes for the compounds studied. From this there came a ‘medicinal chemistry rule’: low-ClogP(<3)/high-TPSA(>75) are approximately 2.5 times more likely to be clean as to be toxic. However, the authors came to realise they had missed something in the analysis, and subsequently referred to it during conference talks. At the 1uM threshold (Figure 3 above) the majority of the compounds were clean. So highly potent compounds, with good bio-availability, enables low dosing is the best route to non-toxic results pre-clinically, irrespective of properties.

Physiochemical drug properties associated with in vivo toxicological outcomes

Bioorg. Med. Chem. Lett. 2008, 18, 4872 – 48755.

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-10-11T14:05:43+00:00

BucketListPapers 62/100: The influence of intrinsic dihedral energetics in small molecule and ligand crystal structure poses

Comprehensive Assessment of Torsional Strain in Crystal Structures

bucketlist 62

In optimising the potency of a small molecule drug lead, a key objective is to refine its solution-phase conformational landscape so the desired binding pose is pre-conditioned and energetically feasible. However, calculating the conformational global minimum of a drug-like molecule is challenging for both ab initio and molecular mechanics (MM) methods; for example, density functional theory (DFT) is too computationally demanding to achieve extensive sampling of drug-sized molecules and MM force fields have limited accuracy. To tackle these challenges, this 2019 study focused on dihedral angle energetics to assess conformational strain in small molecule binding poses and used a combined quantum mechanics (QM)/MM approach with cloud computing to achieve accurate yet extensive torsional sampling. In order to reduce the degrees of freedom in each simulation, drug-like molecules were fragmented into “minimal torsion fragments” (fragments that encompass the dihedral angle of interest, with a 1 atom environment plus any atoms that prevent the breaking of rings or functional groups). The fragments were subject to gas-phase torsional scans by generating MM profiles for the dihedral angle of interest and optimising the fragment geometry at windows along the profile using DFT. The resulting QM energy profiles were compared with ligand crystal structures from the Cambridge structure database (CSD) and the protein data bank (PDB), revealing that the preferred crystal poses were associated with low-strain dihedral angles. As might be expected, the small fraction of poses that resulted in high torsional strain could mainly be attributed to external steric or hydrogen bonding effects. Despite this, these results suggest that intrinsic torsional energetics are overwhelmingly responsible for the preferred crystal structure poses of small molecules and ligands. With this in mind, the authors suggest a useful workflow for the estimation of conformational strain energy in drug-sized molecules: 1) extract the minimal torsion fragment for each rotatable bond of the input molecule, 2) perform torsional scanning to generate dihedral angle energetic profiles for each fragment, 3) estimate the strain energy of each rotatable bond by mapping the input conformation onto the corresponding energy profile, 4) calculate the total conformational strain energy as a linear sum of the individual strains.

J. Chem. Inf. Model. 2019, 59, 10, 4195–4208

2022-06-22T12:45:10+00:00

BucketListPapers 61/100: How molecular shape modelling can aid drug discovery.

Molecular shape influence

This perspective provides a detailed description of the ways in which molecular shape is modelled and utilised to enhance drug discovery. The authors address how molecular shape modelling has impacted three questions essential in medicinal chemistry: “What is the essence of a molecule? What is it made of? What will it do?” Starting with virtual screening, the perspective details how programs like ROCS (rapid overlay of chemical structures) and SQW (SemiQuantitative reWrite) can be used to find new ligands for targets given a known ligand. These programs use atom-centred Guassians, coloured by atom type, to represent molecules as functionalised volumes that can be overlayed and compared. The authors also highlight the importance of molecular shape in lead optimisation; specifically for the identification of bioisosteres for the improvement of pharmacokinetic properties while maintaining or improving target potency. Furthermore, an extensive review of the use of molecular shape modelling in protein crystallography and ligand pose prediction is given, highlighting technologies and examples where both the molecular shape and torsional strain are optimised to provide realistic ligand poses. Molecular shape is also shown to be a useful metric in the design of diverse compound libraries with algorithms developed to design the molecular shape space of interest and cluster compounds into diverse shape clusters. The authors also describe examples of how Guassian-based molecular shape representations can aid in the design of protein-protein inhibitors, which are notoriously hard to design due to the flat nature of the protein interaction surfaces. Next, an alternative method for molecular shape description is introduced that describes the shape as a surface by placing the molecule in a grid of points in space and recording the minimal distances of the points to the molecular surface; a method that has been utilised for 3D QSAR modelling. Finally, the authors present a comparison of several approximate shape methods that provide quick results for pre-filtering large datasets before more exhaustive calculations can be performed.

In summary, understanding molecular shape can make an impact in many areas of drug discovery and a variety of Guassian or surface -based modelling programs have been developed to aid the medicinal chemist. This review offers an extensive description of such approaches and their example uses, and is a great read for those wishing to develop their understanding of the field.

Molecular Shape and Medicinal Chemistry: A Perspective Med. Chem. 2010, 53, 10, 3862–3886

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-10-04T14:05:26+00:00

BucketListPapers 60/100: The General Solubility Equation.

general solubility equation

Compound solubility is one of the key physicochemical properties that is essential to optimise for drug formulation and systemic absorption. While there are several kinetic and thermodynamic solubility assays that can be incorporated into the compound optimisation cascade, it is also useful to employ theoretical calculations for very high-throughput predictions of aqueous solubility. The general solubility equation (GSE), first introduced in the 1980s and later optimised in the early 2000s, uses only 2 parameters (the Celsius melting point and octanol-water partition coefficient) and has been a standard within the pharmaceutical industry. This validation paper from 2001 compares the use of the GSE with a Monte Carlo simulation method on 150 compounds and determines that, on average, the GSE provides solubility predictions closer to experimental values. Considering the speed at which the GSE can be applied compared to Monte Carlo simulations, this highlights the usefulness of the simple equation for guiding compound design.

Prediction of Drug Solubility by the General Solubility Equation (GSE).

Chem. Inf. Comput. Sci. 2001, 41, 2, 354–357

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-09-30T14:00:14+00:00

BucketListPapers 59/100: How can computational chemists make real impact in drug discovery?

Fig2 WhatWorksWhatDoesnt

In this report from 2006, Martin assesses how computational chemists can positively impact drug hunting projects by forming close collaborations with medicinal chemists. The report starts by summarising the computational calculations that were the most popular with Abbott medicinal chemists at the time and showed data that indicated how computationally cataloguing structural alerts as SMARTS reduced the number of flagged compounds within Abbott’s compound library over time. Furthermore, several examples were identified where models with low predictability were still deemed useful. For example, log P predictors were shown to have low accuracy but considered to be “good enough” by medicinal chemists to predict relative log P values within a series. The report also highlights examples where computational chemists offered useful insight that aided decision-making, even when the data or models were inaccurate. An example of this was the observation by a computational chemist that the available data for modelling a compound series had a narrow log P range, which led to the prioritisation of more polar compounds that were subsequently optimised into a lead.

In summary, this report is still an important read for computational and medicinal chemists who work together today, serving as a reminder that drug discovery projects benefit when knowledge and insight is shared between the two fields.

What Works and What Does Not: Lessons From Experience in a Pharmaceutical Company

QSAR & Combinatorial Science, 2006, 25, 1192-1200.

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-09-26T14:00:12+00:00

BucketListPapers 58/100: Time for some Skepticism!

Fig3 Healthy skeptism

“Although the development of computational models to aid drug discovery has become an integral part of pharmaceutical research, the application of these models often fails to produce the expected impact on productivity.” The first line of the abstract of this paper introduces the subject and problem succinctly. This important paper covers into the reasons for this lack of success and covers some critical ground on assessing models and incorporating them into med chem workflows in a timely manner. The paper is on the heavy side of statistics but the charts in Fig. 3 should prove useful for assessing models. Following this paper a blog entry from Pat Walter was published online – this is well worth a read too.

Healthy skepticism: assessing realistic model performance

Drug Disco.Today, 2009, 14, 420-427

Here is a follow up blog that is well worth a read:

https://practicalcheminformatics.blogspot.com/2019/07/how-good-could-should-my-models-be.html

2021-09-23T14:00:00+00:00

BucketListPapers 57/100: Are we as smart and consistent as we think we are?

how chemists choose compounds

These two papers should really make you think. First, in 2004 Pharmacia, submitted lists of compounds to their medicinal chemists to select as part of a compound acquisition initiative. By sending multiple lists (rather than one big list) it was possible to look at the consistency of the chemists in their selection choices. While the authors expected a difference between chemists, they did not expect an individual chemist to be inconsistent between each list. This is food for thought. In Novartis in 2012 sent multiple lists of compounds, but in addition the chemists were asked on what criteria they had chosen the compounds i.e. lipophilicity, size, diversity, novelty. The expectation was that calculations and filters would be applied to reduce the lists to a smaller sets to review by eye. From the returned selections it was possible to look at the spread of these properties. The actual selections did not reflect the criteria the chemists had said they used. Of most concern, nearly all chemists said they used novelty as a select, but only 2 of the 19 actually selected novel compounds. Overall, both studies showed considerable bias in compound selection, again considerable food for thought.

Assessment of the Consistency of Medicinal Chemists in Reviewing Sets of Compounds. J. Med. Chem. 2004, 47, 20, 4891–4896 

Inside the Mind of a Medicinal Chemist: The Role of Human Bias in Compound Prioritization during Drug Discovery. PLoS ONE 7(11): e48476.

2021-09-20T14:21:06+00:00

BucketListPapers 56/100: How have acids and bases faired in drug discovery?

acids and base in drugs solubility

“It is fairly common for drugs to be classified as weak acids or bases or perhaps more accurately as acids, bases, neutral, or zwitterionic.” Often the acidic or basic group is key part of the pharmacophore, and as such tend not be optimised by fine tuning the pKa. This very useful review is a comprehensive study of the effect acidic and basic compounds.  Table 1 to 4 should be printed out by any compound designing chemists, and carried around as a reference. These summaries the effect on ADMET properties of ionised molecules from several dozen papers. The selected plot above (Figure 5) showing the clear effect of having ionisable group on aqueous solubility.  However, having read the rest of the paper you will be left with the view that having a neutral compound as a drug is the best outcome, given that both lipophiliic acids and bases tend to have some kind of ADMET issue.

Acidic and Basic Drugs in Medicinal Chemistry: A Perspective J. Med. Chem. 2014, 57, 23, 9701–9717

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-09-16T14:00:43+00:00

BucketListPapers 55/100: Methyl, Ethyl, Futile – We have all said it…

We distinctly remember when this paper came out, and it was not long after that the phrase entered the common lexicon of the medicinal chemists. The study was not that many years after the Rule-of-Five paper, and within our discipline, the naughties become a decade of looking at compounds and defining further guidelines (some becoming un-useful “rules”). This is a must read, as it discuss the key principal of finding the lipophilicity ‘sweet spot’. This is where binding affinity and absorption are sufficient, but not too high, where metabolism and safety concerns arise. The reason for “methyl, ethyl, futile” phrase is simple because it is too easy to increase the lipophilicity of a compound series and “potency” improves, leading to a false sense of progress on a project. Later on came the concept of efficiency in drug design; getting the most out of each atom and lipophilic group. Read the paper and it will improve your thinking in compound design.

Lipophilicity in PK design: methyl, ethyl, futile.   J Comput Aided Mol Des. 2001 Mar;15(3):273-86. 

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2021-09-13T14:00:29+00:00

BucketListPapers 54/100: The biggest screening libraries ever made: DELs

Picture BLP 54 100

The last post concerned fragment libraries – round built on the philosophy “small fragments can represent massive libraries” at the other end of the scale are DNA encoded combinatorial libraries (DELs).  DELs represent the technological offspring of combinatorial chemistry and molecular biology with a little classical protein biochemistry for good measure. With split pool synthesis to make vast libraries and encoding the sequence of chemistry used in a DNA sequence attached to the compounds, huge libraries can be made and potent ligands identified.  Chromatography with the protein target as ‘bait’ to fish out the most potent compounds followed by PCR to sequence the DNA tag establishes the identity of the best binders.  If you can do affinity chromatography with your protein target, DELs represent the other extreme approach to lead generation.  The first paper is a pure classic – Brenner and Lerner’s PNAS publication contains the essence of the technique in highly readable form.  It contains the brilliant line “we recently, in principle, solved the synthetic procedure for peptides”.   But actually to industrialise takes another 17 years.  There has been a huge number of synthetic chemistry devils to outwit in making the method scalable and Morgan et al’s 2009 paper shows one version of the production and screening of billion compound libraries and the identification of inhibitors reduced to practice.

“Encoded combinatorial chemistry”, Brenner and Lerner,  Proc. Natl. Acad. Sci (1992), 89, 5381-5383

“Design, synthesis and selection of DNA-encoded small-molecule libraries”, Morgan et al, Nature Chem. Bio. (2009), 5, 647 – 654

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

 

2019-12-06T13:30:09+00:00

BucketListPapers 53/100: The rise and rise of Fragment Based Drug Discovery (FBDD)

1YSG SAR by NMR ligands

http://www.rcsb.org/3d-view/1YSG

FBDD is now an established methods of drug discovery having resulted in drugs delivered to patients and multiple compounds in clinical trials.  For groups without access to a compound collection or where the belief is that the target belongs to a class where you have few ligands, FBDD is a logical choice.  The key requirement is that you can access structural information to drive synthesis to make the small, weak ligands more potent.  FBDD has also provided a framework for people to think about what constitutes a good ligand via the debate round ligand efficiency, and how to improve potency.

The first paper to read is Hadjuk, Fesik et al’s “SAR by NMR”

“Discovery of Potent Nonpeptide Inhibitors of Stromelysin Using SAR by NMR”  J. Am. Chem. Soc., (1997), 119, 5818–5827

And then to follow up:

Murray & Rees,  Nature Chemistry (2009), 1,187–192

Congreve, et al, J. Med. Chem. (2008), 51, 3661–3680

And finally:

“Twenty years on: the impact of fragments on drug discovery”

Erlanson,  Fesik, Hubbard, Jahnke & Jhoti, Nature Reviews Drug Discovery (2016), 15 , 605-619

 

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2019-12-03T13:30:38+00:00

BucketListPapers 52/100: Structural alerts for Mutagenicity – a must for every compound designer.

Picture BLP 52 100

We feature here, as the bucket list paper, one of first papers to publish in this key area by John Ashby, but in fact there are number of vital publications. Observations and testing results in the critical Ames assay were taken by the likes of Ashby and Tennant to derive and categorize a set of structural alerts for DNA reactivity that can identify potentially mutagenic compounds. There is some danger in doing this as Ashby states in his paper “It is obviously dangerous to simplify so complex an issue as chemical-structure/biological-activity relationships in chemical carcinogenicity and mutagenicity.”  None the less it is important to know which chemical groups frequently cause this type of toxicity to ensure correct screening and due process; avoiding them altogether is best.

We include a few other papers that work further to increase knowledge and develop computer models to predict tox. The tables in these papers should be printed out and stuck on the wall above your desk!

 

“Fundamental structural alerts to potential carcinogenicity or non-carcinogenicity” Ashby Environ. Mutagen. (1985),7, 919-921

 

This paper uses corresponding Ames test data (2401 mutagens and 1936 non-mutagens) to construct new criteria and alerts. SMARTS string representations of the specific toxicophores are available in the Supplementary Information:

 

“Derivation and Validation of Toxicophores for Mutagenicity Prediction” Kazius, McGuire, Bursi J. Med. Chem. (2005), 48, 312-320

 

And in vivo rat studies

“Structure−Activity Relationship Analysis of Rat Mammary Carcinogens” Cunningham, Moss, Lype, Qian, Qamar & Cunningham Chem. Res. Toxicol. (2008), 21, 10, 1970-1982

 

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2019-11-21T13:30:14+00:00

BucketListPapers 51/100: Neural Networks – back where it began

Our previous entry talked about the current theme of using deep neural networks, however it’s worth remembering that the field has been here before.  For a really clear and thoughtful exposition of the use of artificial neural networks see Salt and Livingstone’s 1992 paper, which in 6 clear pages covers the essentials of the technique, examples of how ANN’s can fit to different functions, many of the issues and two case studies. For an even more succinct and prescient view, Ichikawa’s 1990 paper is a great read particularly for the phrase:

 

“the difficulty of the convergence is not caused by the structure of the network but the quantity of the information included in the given data”.

 

Salt, Yildiz, Livingstone and Tinsley, “The use of artificial neural networks in QSAR.” Pestic. Sci. (1992) 36, 161–170

Aoyama, Suzuki and Ichikawa,  “Neural Networks applied to Structure-Activity Relationships” J. Med .Chem. (1990), 33, 905–908

 

#BucketListPapers #DrugDiscovery #MedicinalChemistry #BeTheBestChemistYouCanBe

2019-11-18T13:30:26+00:00
Go to Top