Monday, 4 January 2016

Drugs and the Bipolar Medicinal Chemist

According to Dr Christopher J. Burns at the Walter and Eliza Hall Institute of Medical Research, a good Medicinal Chemist (MC) is a polymath. With a strong background in synthetic organic chemistry, a good MC should also have a working knowledge of the biology of their therapeutic area of interest; drug screening and biochemistry; drug metabolism and distribution; the interface between chemistry and toxicology; and intellectual property and competitive positioning.
Formerly known as both the Bipolar Economist (BE) in 2000 and Bipolar Statistician (BS) in 2004, he was reincarnated as the Bipolar Chemist (BC) in 2015. Consequently, he now possesses expertise in a number of different disciplines, namely: accounting and commercial law; economics and econometrics; biostatistics and bioinformatics; computational biology and computational chemistry; the list goes on...However, outside his scientific interests, he also LOVES music, poetry, philosophy, psychology and theology. Given his passion for both the arts and the sciences, the BE/BS/BC is a self-proclaimed polymath, and believes he has what it takes to be a good MC.

Economics and statistics of love

What makes a 'good' drug?
Drugs should meet the following requirements before being released on the market for human consumption.
  • Be potent against its target to minimise the dose required
  • Be selective over other biological processes to reduce risk of toxic side-effects
  • Display good absorption, distribution, metabolism, excretion and pharmacokinetics (ADME-PK) so that dosing is convenient
  • Possess an acceptable safety profile so that it can be dosed without undue toxicity
  • Possess acceptable physicochemistry so that the drug is stable and can be formulated
  • Be patentable so that it can be commercialised
  • Be synthetically feasible so that it can be manufactured on a large scale
Medicinal chemists are responsible for the design and discovery of drugs that meet these criteria.

Courtesy of Chemistry in Australia magazine, February 2015 (p.23)

How to discover drugs?
Drug discovery starts with identifying a biological process that can be modified to therapeutic benefit. This typically involves the inhibition of a certain function of a protein or protein complex (e.g. ion channel, receptor, enzyme) that has been shown to drive disease pathology. Once the drug target has been identified it is then necessary to find the chemical compounds that interact with the target, thereby blocking the aberrant activity associated with the disease. This is generally achieved through a process called high-throughput screening (HTS). After identifying 'hits' from HTS, the medicinal chemist's job is to modify these hit compounds to address issues of potency, selectivity and pharmacokinetics; this is done by way of drug optimisation.

What is drug optimisation?
Drug optimisation is a systems development cycle that involves design-synthesis-testing-analysis.
  • Design: At each turn in the cycle, compounds are designed to improve the desired properties while balancing potential risks from making changes to the compound. Molecular property predictions (e.g. lipophilicity, basicity, molecular weight) are used to increase the likelihood that new compounds will possess good physicochemical and ADME-PK properties.
  • Synthesis: Designed compounds are typically synthesised on milligram scale for initial testing. Compounds must also be synthesised in a robust and efficient manner to ensure that analogues can be easily prepared.
  • Testing: Preliminary screens will test potency and selectivity in biochemical and cellular assays. Further study is then undertaken via a screening funnel (as shown below).
  • Analysis: Data for a suite of compounds from the assays undertaken is analysed to determine the effect of each individual chemical modification on the overall activity profile of the drug.

Courtesy of Chemistry in Australia magazine, February 2015 (p.24)

Compounds that successfully make it through the funnel can then undergo more elaborate preclinical profiling, which may include broader toxicity assessment, human material studies and process chemistry development (Burns, 2015). 

What drugs are the Bipolar Chemist interested in studying?
Apart from Bipolar Disorder, the Bipolar Chemist is also afflicted with two other chronic illnesses, namely Hypertension and Haemochromatosis. A list of his medications and treatments are given below.

Bipolar Disorder 
  • Epilim (sodium valproate) - mood stabiliser 
  • Seroquel (quetiapine) - atypical antipsychotic 
  • Lexapro (escitalopram) - selective serotonin reuptake inhibitor (SSRI) 

  • Coversyl Plus (perindopril erbumine/ indapamide hemihydrate) - angiotensin converting enzyme (ACE) inhibitor / chlorosulphamoyl diuretic  
  • Deferoxamine (chelating agent used to remove excess iron from the body)

Previous medications include Lithicarb (lithium) and Saphris (asenapine). His psychiatrist has also considered prescribing him with Lamictal (lamotrigine) and N-acetylcysteine (NAC) as adjunctive therapies.

Is it safe to combine these drugs?
Certain combinations of medicines (prescription or otherwise) cause side effects that do not arise when individual substances are taken alone. Studies published over the past two decades suggest that such  "drug interactions" cause more than 30 percent of side effects from medications (Wapner, 2015). Unfortunately, pharmaceutical companies cannot always predict when a new drug will mix badly with other medicines - not to mention supplements or foods - and so unexpected deaths are sometimes the first warning sign. In response to this "deadly drug combination" problem, new software and gene analysis tools are being developed to predict which medicines can become harmful when taken together. Bodies like the Precision Medicine Initiative (a project led by the National Institute of Health) and Center for Drug Evaluation and Research (at the Food and Drug Authority) are developing the following:
  • A national databank that facilitates genetic testing and analysis by local pharmacists to identify key genetic variants that affect our body's ability to process different drugs;
  • Computer models that use clinical research data to calculate how one drug will alter the concentration of another when both drugs are metabolised by the same enzyme
The aim of these initiatives is to give patients clearer warnings on drug labels about possible drug interactions and easy-to-understand recommendations on how a dose should be altered (based on computer modelling) when additional drugs are introduced.

"N-of-1 medicine
Rather than trying to design drugs that fit best for a whole population, the Bipolar Chemist's vision is to create his own personalised treatment to combat his illnesses in isolation. Only time will tell whether this polymath can deliver on this promise. 

Burns, Christopher J. (2015) Medicinal Chemistry: Central to Drug Discovery, Chemistry in Australia, Feb 2015, 22-25
Wapner, Jessica (2015) Deadly Drug Combinations, Scientific American313, 29-30

Saturday, 2 January 2016

Happy Mendeleev Day

To commemorate IUPAC's announcement that Period 7 of the Periodic Table of Elements is finally complete, thanks to the 'discovery' of four elements 113, 115, 117 and 118, the Bipolar Chemist will drink coffee from this mug for 4 days.

Mendeleev says welcome to ununtrium (Uut), ununpentium (Uup), ununseptium (Uus) and ununoctium (Uuo).

Wednesday, 30 December 2015

How to count synapses with machine learning

Once upon a time, the Bipolar Chemist was formerly known as the Bipolar Statistician. His primary interests were in statistical modelling of time series data (ARCH/GARCH), as well as count statistics using the discrete Poisson probability distribution, among others. The moment the Bipolar Chemist heard about this great new discovery from the hallowed halls of Carnegie Mellon University, he immediately sent word to its discoverers requesting a possible pipeline collaboration. Modelling such rich and exciting data would be a dream come true for the Bipolar Chemist/Statistician.

Synaptic density analysis using a new machine-learning algorithm
Image credit: Saket Navlakha and Alison Barth; Journal of Neuroscience.

This image shows synapses in the somatosensory cortex stained with ethanolic phosphotungstic acid and visualised using electron microscopy. Synapses were identified using a Carnegie Mellon-developed machine learning algorithm that enables a high-throughput analysis of experience-dependent changes in synapse properties across the cortical column. For this image, candidate synapses were selected from electron micrographs and aligned, then pseudocoloured for contrast. Researchers are now beginning to use this data to develop new hypotheses about how synapses are organised in the neocortex in response to sensory input. 
This research is the latest example of how researchers with the Carnegie Mellon's BrainHub initiative are combining their expertise in biology and computer science to create new tools to advance neuroscience. The technique uses a special chemical preparation that deeply stains the synapses in a sample of brain tissue. When the tissue is imaged using an electron microscope, only the synapses can be seen, creating an image that can easily be classified by a computer program. Researchers then use machine learning algorithms to identify an compare synapse properties across a column of the cerebral cortex.

Image credit: Howard Lam, University of Sydney

Tuesday, 29 December 2015

The Bipolar Chemist and Zing Conferences

The Bipolar Chemist has just registered for the following scientific conferences in the US and Ireland in 2016. He is hoping to meet like-minded computational chemists, biophysicists, biostatisticians and neuroscientists to gain further knowledge and insight into bipolar disorder, particularly in relation to:
  • cAMP signal transduction
  • GPCR modelling and simulation
  • Voltage-gated ion channels and transporters
  • Optogenetics of photoactivatable adenylate cyclases (pACs)
  • Whole-cell computational approaches to synthetic biology
  • Neuropsychopharmacology and cognitive neuroepigenetics
  • Fractal symmetry of protein interior and exterior
  • Clifford algebra and DNA structure
Mad Poster Scramble at the Molecular Modelling 2015 conference @ UNSW 

Structure-Based Drug Design: 21st - 24th February 2016
Structure-based drug design (SBDD) has evolved a great deal over the past 30 years since the concept of the "Lock and Key" hypothesis was first reduced to practice in the HIV-1 protease field, leading to the rational design of a number of molecules that are at the forefront of effective therapy. Evolution of techniques such x-ray crystallography, NMR and computational chemistry have enabled a more informed understanding of binding events, encouraging many groups to employ SBDD strategies to wide range of drug-relevant targets.
Zing's inaugural SBDD event will showcase many of the advances made in structural biology, fragment-based methodologies and computational chemistry, demonstrating how these have been used to enable drug discovery programs in a range of drug target classes from enzymes, to more complex G-protein coupled receptors (GPCRs) and protein-protein interactions (PPIs). The conference will also feature some of the emerging technologies in the field, which will serve as a glimpse into the future of drug discovery as SBDD becomes more diverse and unrestrained.
You will enjoy the opportunity to engage with many of the pace-setters in the SBDD field, leaving with fresh ideas and concepts that will help to continue to build success in your drug discovery efforts.

Structure-based drug design @ Santa Rosa, California

Propagation in Neurodegenerative Disease: 8th - 11th August 2016

Neurodegenerative diseases are characterised by the progressive decline of neuronal functions in a spatiotemporal fashion, leading to the worsening of neurological symptoms, in some cases, with devastating speed. This is accompanied by gradual intensity of neuropathological findings from more to less affected regions in the nervous system. The molecular pathway driving progressive decline of disease symptoms and pathology is not yet understood. However, the amplification of pathological protein assemblies, which spread from cell-to-cell, may well explain these phenomena. We will bring together neurologists, neuropathologists and basic scientists from a wide variety of neurodegenerative diseases to discuss the implications of these mechanisms for building disease models and therapeutic strategies. We will also discuss the similarities and differences between the different classes of neurodegeneration, comparing to prototypical prion diseases. Lastly, we will examine the role of inflammation and cellular stress in potentiating the initiation and spreading of pathology.

Propagation in neurodenerative disease @ Dublin, Ireland

Nucleic Acids: 2nd - 5th December 2016
Nucleic acids are the cell's informational macromolecules. DNA is a repository of genetic information and must accurately copied once and only once in each cell cycle. Its integrity is vital to the cell, and it is the only molecule that is repaired if damage occurs. DNA undergoes a recombination that creates diversity and leads to evolution, yet also provides an important method of repair. In eukaryotic cells DNA is packaged into chromosomes in a way that allows it to be packed into the cell nucleus, yet accessible to the cell's machinery for reading out its genetic information. By contrast RNA is the 'working substance' of genetics and an extremely versatile molecule. Of course it is the messenger that passes the information between DNA and protein synthesis, yet it does so much more. It is the key component of the ribosome, as well as the tRNA species involved in translation. RNA can act as a molecular switch responding to small molecules in order control gene expression, and can also accelerate chemical reactions by a million fold or more in the manner of an enzyme. Increasingly we realise that RNA is also involved in critical and complex regulatory processes.

Nucleic acids @ Tampa, Florida

Bipolar Disorder and Adenylate Cyclase 2: A Fairy Tale

Once upon a time, in a land far, far away, there lived a complex mental illness whose name was Bipolar Disorder. Affecting about two percent of the population, the illness is characterised by extreme “mood swings” that cycle between episodes of mania, hypomania and depression. Then on one fateful day, the Bipolar Chemist entered this faraway land, armed with many magical methods, namely homology modelling, molecular dynamics and site-directed mutagenesis, to name but a few. He was very keen to work his magic on a particular transmembrane protein called adenylate cylase 2, an enzyme implicated as a risk factor in a recent genome-wide association study (GWAS). After receiving a prestigious School of Science and Technology Special Project award from the University of New England in 2013, the Bipolar Chemist’s lifelong dream of learning molecular modelling and simulation finally became a reality via a four-week research traineeship. With the help and support of his beloved supervisor, he worked day and night on the Swing The Mood project, examining the protein-structure relationships of adenylate cyclase 2 with respect to cyclic adenosine monophosphate (cAMP) signal transduction, a key biological pathway involved in the development of bipolar disorder. Fast-forward one year to today and alas, the Bipolar Chemist’s first ever poster is here to stay! And they all lived happily ever after…