Gillman’s AD algorithm


This commentary presents my antidepressant treatment algorithm which incorporates concepts from Bayesian reasoning and ‘critical path analysis’; that makes it is a ‘two-lane’, or multi-lane, clinical pathway. It emphasises procedures which some algorithms neglect. The appendix discusses problems concerning assumptions about the pharmacology and methodology relating to the derivation of treatment algorithms, especially the mis-leading conclusions produced by meta-analysis.

Key words: algorithm, guideline, critical path analysis, meta-analysis, meta-analysis problems, anti-depressants, drug treatment, pharmacology, treatment resistant depression, severe depression, melancholic depression, psychotic depression, SSRI, SNRI, TCA, MAOI

Gillman’s AD algorithm

First, my basic fast-lane algorithm for ‘biological’ depression (no AD-free periods required for any step!).

Step 1. Sertraline or Nortriptyline: 6 weeks.

Step 2. A) Sertraline combined with Nortriptyline 6 weeks.

Step 3). Tranylcypromine.

This is elaborated below.

Background: theory and considerations

I receive many enquiries about minimally effective AD treatment* from unfortunate people who have been on various similar antidepressants for a long time with little regard for what is sensible and reasonable, and little regard for their ongoing partially-treated illness and their suffering and deteriorating life circumstances (like four successive SSRIs, over years — pointless, waste of time (1)). It is of great concern that a proportion of doctors do not think critically and logically about advancing people to more effective treatments in an expeditious manner.

*NB. Opinion in this commentary refers to AD treatment for serious ‘biological’ depression, different criteria may apply to anxiety and mild forms of depression.

This commentary does not consider non-pharmacological interventions which are effective and may be the preferred 1st option for many people, especially those with less severe and persistent symptoms.

Thinking critically and logically about the expeditious progress of treatment benefits from incorporating the precepts of ‘critical path analysis’, which not all algorithms accomplish. A measure of knowledge and resolve are required to achieve that because there is a strong natural reluctance to change a treatment, even if it is only partially effective. Doctors need to be aware of how the psychology of ‘risk aversion’ and ‘negativity bias’, influence their advice and decisions.

Guidelines and algorithms also have a downside: they promote intellectual laziness; they deal in generalities not individuals; they may obfuscate the fact that treatment decisions about individuals are the sole responsibility of the treating doctor; they can stifle innovation and originality and foster a self-fulfilling conformity.

The explanatory justification for failure of ‘I followed the guidelines’ is inadequate (morally, intellectually and legally). One might regard it as the medical equivalent of the ‘Nuremberg defence’, (just following orders).

‘Qui audet, adipiscitur’ — Who dares, succeeds.

‘Qui audet, adipiscitur’ became well-known after the British SAS adopted it as a motto using the following — I would argue incorrect — translation: ‘Who dares, wins’.

Qui audet, vincit is more precisely translated as ‘He who dares, wins’. After all, what did Caesar say? ‘Veni, vidi, vici’.

But do not take my word for it, I came bottom in the Latin scholarship class. The headmaster returned one of my pieces of Ovid translation with a mark of 4/20; followed by the comment: ‘very good’! At least he had a sense of humour. My Greek was no better (πίστεψέ με).

Anyone who has read much about depression will have heard of treatment algorithms, things like the widely-discussed STAR*D research. Algorithms can be useful even if they are, perforce, mainly based on consensus and ‘expert opinion’, because of lack of firm evidence to choose between many of the alternatives.

It must always be remembered that no matter how authoritative such algorithms and guidelines may be perceived as being, that they are not better than the judgement of an experienced scientific clinician. There are significant variations in pronouncements on the same topics by different sets of guidelines (2-4): in my considered opinion (IMCO), this reflects the misleading and pseudo-scientific nature of meta-analysis (see below).

It is insufficiently recognised that guidelines constrain the flexibility of clinical practice and produce a self-fulfilling sameness in treatment approaches (3, 5). For instance, this applies to the use of MAOIs: they are only suggested as a ‘last-ditch’ measure, they are hardly ever used, therefore there is virtually no research about them, therefore there is no evidence to support their use. Not a productive way of furthering clinically-based research.

There is another distinctly negative aspect to guidelines and algorithms: regulators and policy-makers may use them to censor and control doctors, and the use of medicines. This is clearly already happening and is exemplified by doctors who refuse to prescribe MAOIs, usually because they are afraid.

The usefulness of algorithms can go beyond deciding which drug to use, and thereby profit from the precepts of critical path analysis (see below).

In this commentary, I am not going to go into detail concerning the justification for the choices I present, because my reasoning and the evidence is in the general literature, and my scientific papers, and elsewhere on this website.

It is a complex area, so background explanation is required, especially for people who have not read extensively around the subject, nor read what I have written previously.

First, I am referring exclusively to the treatment of people who have drug responsive illnesses and who exhibit the key central changes of anergia and anhedonia, persistently and consistently, over a period of a month or more, and to a degree of severity which has impaired their social, leisure and work function to a consequential extent. Such an assessment is best made by an experienced psychiatrist: doctors are giving drugs to many people who do not need them (6), this tendency is contributed to by pressures from society’s expectations and the economics of healthcare, and you have guessed it, drug company advertising. A connected and toxic trio of factors.

There is a compelling logical argument for stratifying the speed of progression through the alternatives in the algorithm (it is ‘multi-path’ rather than two-path) depending on the degree of severity of symptoms, and particularly the degree of functional impairment. When that is sufficiently severe to endanger relationships, work and people's financial security, there is less justification for continuing minimally effective courses of drugs and engaging ‘fine-tuning’ strategies which may be less likely to produce substantial improvement.

This is how and where the focus on functional impairment, and the rapidity and adequacy of improvement, relate to the ‘critical path analysis’ dimension of the algorithm.

Whilst most doctors would agree that urgent ECT is strongly indicated for a severely suicidal patient who has stopped eating, there seems to be a less clear appreciation of the general need to take account of the medium-term destructive influence of the illness in less serious cases. That is why an algorithm benefits from incorporating a multi-lane pathway with decisions based on the extent and speed of response and risk of deterioration.

A strong indicator of ‘biological’ depression is real improvement in core symptomatology (7-9) within 3 to 4 weeks of starting an antidepressant. A less decisive response, over a longer time, is not good evidence of a cause-effect relationship between taking the drug and improvement, especially when that improvement is in ‘non-core’ and less illness-specific symptoms. Failure to recognise the key time relationship between drug administration and improvement (or adverse effects) is a failing of much depression research.

If a drug response is going to occur, that will usually be evident to an experienced observer within 2-3 weeks of attaining a therapeutic dose. It is impossible to mention assessment of improvement without noting an ‘elephant in the room’: the problem of the sensitivity and validity of rating-scales generally, and the much mis-used, but ubiquitous, ‘Hamilton’ rating scale (10, 11), on which most drugs trials rely (11-14). It is an inadequate instrument that should have been superseded long-ago (12, 15-17). It was not well-designed to be sensitive to change in severity of symptoms, including the ‘core’ symptoms, à la Parker and colleagues.

Results from drug trials using the DSM diagnosis of MDD are prone to include too great a percentage of cases that are unlikely to be ‘biological’ depression — see, for instance: (9, 18-22). Also, bias from sponsorship (a huge proportion of trials are drug co. sponsored) and cases recruited in primary care*** both muddy the waters even more — to the point of opacity — yet such trials are all grist-to-the-uncritical-mill of meta-analysis and the formation of guidelines and algorithms.

In summary: we know we are including inappropriate cases in trials and we know we are assessing change with a poor and outdated instrument.

Is that smart?

*** It is inconceivable that such cases are predominantly ‘biological’ depression. That explains why mainly sedative drugs like mirtazapine appear effective: Even a small proportion ‘non-biological’ cases contaminating the sample will invalidate most conclusions.


If there is space for another ‘elephant in the room’ it is meta-analysis. This procedure is the 21st century successor to Phrenology and Psychoanalysis (see here for the history of those two long-accepted but groundless notions).

IMCO meta-analysis is a capricious and deceitful siren (an appreciation of its major limitations is growing, see below): I suggest following Ulysses course of action (block your ears – for those unfamiliar with the myth). Look at the painting (in the link), an allegory if ever there was one – the decaying corpses beside the sirens (of clinical trials?), and the disintegration of the painting itself. One could hardly invent it.

Some time ago I coined the expression ‘Penrose stairs with drugs’ see: and Heres et al. (23). They showed ‘olanzapine beats risperidone, risperidone beats quetiapine, and quetiapine beats olanzapine’ and the same illogical circle (of A>B>C>A) can be replicated with almost all the AD drugs. It is clear to anyone of a scientific mind that many trials are poor quality data and that over-interpretation is omnipresent (24).

As I stated in ‘Atypical Anti-Psychotics and Humpty Dumpty’: It is important to keep repeating that no meta-analysis can be better than the original data upon which it depends (25, 26). That is the old computation saying “GIGO”, “garbage in, garbage out”. What would Charles Babbage have thought? well, we know, because he told us. 

“On two occasions I have been asked, —"Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?" ... I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.Babbage, Passages from the Life of a Philosopher.

Since it has been my view for a long time that clinical trials (many of which are ‘pseudo-scientific’) have unjustifiably overshadowed good clinical science and clinical experience, I am much heartened to see, in researches for this commentary, that some researchers are supporting a re-balancing of that question. The following references are eloquent testimony to the up-welling of the view about the unreliability of meta-analysis, and the appropriateness of putting more weight on scientifically conducted clinical practice (4, 25, 27-32). Several very savvy authors are represented in these references (Altman, Ioannidis), which are well worth checking out.

I will retain a healthy respect for my own judgement based on clinical science and sound Bayesian logic.

Critical path analysis

Critical path analysis is a method for formalising the structure and timing of a task and focussing more clearly on the assessment of its’ progress and the means of quantifying that and the timing of key decisions affecting the process. It has not been utilised much in medicine (33).

To incorporate precepts from critical path analysis a treatment plan it needs to focus on:

  1. A pre-defined definition of the desired outcome
  2. Objective assessments of the baseline state (not self-rating scales) including symptoms, signs and functional activity assessment covering work, social and leisure domains.
  3. An assessment of the degree of risk of adverse outcomes engendered by the illness (e.g. loss of job etc.), and by any treatment.
  4. Objective assessments of the intermediate stages of severity of the illness (at defined treatment changes).
  5. Predefined time periods for achieving objectives at any treatment stage.

Both doctor and patient may consider, at each visit:

  1. For how long has the drug been administered?
  2. For how long has the present dose been the same? (Appendix Note 1)
  3. What degree of improvement has so far occurred from baseline or worst state?
  4. What degree of risk of severe or prolonged impairment of work social and leisure activities is there in the current situation (i.e. loss of job, relationship separation, etc.)?
  5. Is that improvement, and the degree of reduction of the risks, sufficient to justify continuation of this treatment (or this dose)?
  6. How expeditiously should we progress on the ‘critical path’?

An important aspect insufficiently prominent in some algorithms (and treatment plans) is a clear sense of purpose and urgency about achieving full remission and avoiding potentially irreversible life changes (i.e. before peoples’ lives fall apart).

There is a too easy and uncritical acceptance of partial improvement and of continuing in that state with unchanging treatment (Appendix note 2).

A particular impediment to the expeditious progress of effective treatments concerns widespread misunderstandings about which antidepressant drugs are safe in combination and the necessity for washout periods when changing from one drug to another (most aspects of such interactions are covered in the relevant sections on this website, e,g,

Another element of many of these algorithms is deficient discussion of combination treatments and statements about drugs which are possibly less effective. These are matters that an experienced and knowledgeable clinical psycho-pharmacologist is well positioned to advise on.

IMCO, difficulties with algorithms are an inevitable consequence of schemes devised by committees, whose members may have an insufficient depth of knowledge about pharmacology (see below).

Some academics will be miffed by that comment; but it is true, witness the extensive mis-understandings about MAOIs and serotonin syndrome that still endure. They should be ashamed by their poor level of pharmacological knowledge.

Gillman’s AD algorithm

This algorithm is a ‘two-lane’ choice*, a fast lane and a slow lane. The two-lane analogy helps people conceptualise what approach to treatment suits them best, depending on their attitude to life and risk** (do you usually drive in the fast lane or the slow lane?). Clearly, if functional impairment is producing substantial risk of (potentially irreversible) changes in family, work or financial status (as it so often does) then the a faster-lane pathway (pathway-a) will be preferred over a slow-lane (b).

*It is in fact a multi-lane choice.

** I often used to say ‘Slow and steady wins the race, or, nothing ventured nothing gained’, your choice.

Step 1. Sertraline‡ or Nortriptyline*

See: ‘When to consider avoiding SRIs as first choice’ in:

And, re Sertraline as 1st choice SSRI (34, 35) and:

‡It is unlikely to be useful to exceed the recommended dose with any SSRI, with the possible exception of Sertraline, its weak DA re-uptake potency may confer an advantage at doses around 200 mg or more. Sertraline also has the useful advantage of linear pharmaco-kinetics even at high dose levels (36, 37).

*NTP is the pharmacological ‘gold-standard’ TCA (38), see:

Step 2. An SNRI strategy. This may utilise a single drug, e.g. venlafaxine, as has been fashionable for some years now, or a combination. Gillman’s algorithm strongly favours two separate drugs (for reasons explained), as in step 2a).

Step 2a) Sertraline and nortriptyline (NTP)*/reboxetine or 2b) clomipramine (CMI)** (SNRI strategy). Or (des)venlafaxine*** or duloxetine etc.

*Sert 50 mg + NTP 50 mg may be sufficient but full doses of sert 200 mg and NTP 150 mg may be used providing due caution is exercised about NTP levels (in view of mild 2D6 inhibition of NTP metabolism by high levels of sert). Therapeutic drug monitoring (39, 40) of NTP levels is increasingly desirable if sert in increased beyond 200 mg daily.

**See note on CMI:

***Venlafaxine has several disadvantages (relatively more toxic, especially in the elderly (41)) and I remain unconvinced that it is a) any better, or b) is a fully effective SNRI, and the evidence for its’ superiority is poor, for evidence and refs see:

Step 3a). from Step 2a) cease sertraline, continue nortriptyline*, after one week start tranylcypromine** (TCP); or b): cease CMI, then after washout  1–2 weeks start TCP***

*For detailed discussion of why it is perfectly safe to combine tranylcypromine with nortriptyline see section on this website re MAOIs (e.g., here) and my published papers. MAOI/TCA interaction mis-understandings are the prime exemplar of the poor pharmacological knowledge  of psychiatrists.

** The need for 1– 2 week washout is a powerful reason for using SERT+ NTP, rather than Ven or CMI as the ‘SNRI’ strategy.

*** Or, in some cases phenelzine

Step 3b) To either to 2a or b) Use preferred augmenting strategy. There is not strong evidence favouring a particular choice, as yet. Add Li† + L-tryptophan, quetiapine, olanzapine, aripiprazole or T3 (lamotrigine may have a place here, especially if BPD is known or suspected).

†I think there is a strong case for ceasing Li augmentation within 4 weeks if there is no clear response. Logically a similar approach applies to aripiprazole and other augmenting strategies.

Step 4a) ECT, then after course finished immediately re-introduce the most effective previous drug strategy, + Li if not previously used.

Step 4b) Augment the TCP with: Li‡ +/- L-T +/- NTP* or bupropion (lamotrigine may have a place here, esp. if BPD known or suspected)

*either just continue NTP from step 2a) or cease it after stabilisation on TCP and only re-introduce it if needed.

Notes on Gillman’s algorithm

At no stage is an AD free washout needed in my algorithm. However, if one chooses venlafaxine etc. or CMI in step 2b) one then has a gap to cover (e,g, by dove-tailing doxepin* or NTP).

*Doxepin is an H1 sedative, not an AD (see (38).

My preference after step 2a) was to go straight to TCP, rather than expend time on strategies that my experience indicated were less likely to lead to decisive results, i.e. with various augmentation options, e.g. lithium (see below). If TCP (or phenelzine) was not satisfactory then augmentation can be undertaken at that stage, or after ECT.

A critical path analysis from my experience suggests that the pay-off with augmentation* may not be worth the delay in reaching remission: this a where a careful assessment of the potentially irreversible aspects of functional impairment (work, marriage, finances) usefully guides the speed of progress through the algorithm. Hence the reduced emphasis on Bupropion (42, 43), which is widely preferred (in USA) especially in BPD. It also highlights that augmentation is often continued, despite the problems with medium-term SEs, long after it has been seen to fail.

*The term augmentation can be held to mean the addition of a drug not usually regarded as an AD in its’ own right, as opposed to combination which usually refers to addition of a drug that is regarded as an AD in its own right.

In step 2a) sertraline + nortriptyline is preferred (especially because it facilitates smoother transition to TCP, but also because it enables separate adjustment of SRI and NRI components (which cancel out each-others’ SEs to a useful extent, e.g. 5-HT is GI pro-kinetic which opposes the anti-kinetic effect of TCAs & NRIs).

It has been correctly noted that various combinations risk substantial increases in the SE burden for patients (44). However, as observed elsewhere, it is not valid to generalise about heterogeneous classes of drugs (like TCAs and ‘atypical anti-psychotics’). Mixing many of the old TCAs with some SSRIs is not a good idea at all, but nortriptyline (and sertraline) is quite different. It is a question of knowing your pharmacology, and most aspects relating to this are discussed elsewhere on this website (e.g. see menu heading ‘Interactions’, and in my published papers (38, 45-54).

There has been an ebb and flow of opinion about combinations over the last three decades. Unfortunately, much of the opinion expressed about combinations has been unduly influenced by doctors whose knowledge of pharmacology is inadequate (e.g. see Charpeaud below*). I find it hard to suppress a wry smile when I see the latest opinion about combinations, which champions a combination of an SSRI with reboxetine (55).I have written about this elsewhere (56), but in brief I started using combinations of sertraline with both nortriptyline and reboxetine back in the 1990s. Most patients seemed to prefer for the former, because they found reboxetine made them feel hyped-up and agitated (similarly to an amphetamine effect), whereas nortriptyline did not. See also:

Step 3b) When using ones’ preferred augmenting strategy, the critical path requirement is that an objective assessment of pre- and post-augmentation clinical state is recorded and that a pre-defined degree of improvement is attained in a pre-defined time-period, or that the augmentation is ceased.

Californian rocket-fuel

I am aware of the popularity of the ‘Californian rocket-fuel’ combination (Venlafaxine + Mirtazapine) which some might choose to use in step 2b), however my experience of the superiority of sertraline + nortriptyline never encouraged me to try that combination very often, especially because of my scepticism about the claimed properties of both of those drugs and the greater toxicity of venlafaxine. It is also important to appreciate the extent of the deception concerning the bogus pharmacological data about Mirtazapine, see:

*Charpeaud recently reported from France (57) that ‘augmenting SSRI/SNRIs with mirtazapine/mianserin has become the most recommended strategy of antidepressant combinations. Augmenting SSRI with tricyclic drugs is now a less recommended strategy of antidepressant combinations given the increased risk for the occurrence of pharmacokinetic drug–drug inter-actions and adverse effects’.

This is yet another example of ‘lumping’ (i.e. failing to recognise that the TCAs are a markedly heterogeneous group) and ignorance of pharmacology and interactions.

I meam requiem doleat.

Lithium augmentation

The history of lithium augmentation goes back rather further than most people seem to remember. Its use requires caution and care and a discussion of relevant considerations is in most good standard texts, (and here). We were using it extensively in London in the late 1970s and I must have been involved in treatment of hundreds of patients using this technique by the time I had been in Australia for several years in the mid-1980s. Once I was in private practice I found myself using it less and less frequently, partly because decisive improvement was uncommon and partly because it was a relatively costly and troublesome procedure (58).The trend, over more than three decades, is of a weakening of the evidence for benefit (59), but Nelson’s & Bauer’s reviews’ (based on low numbers) are more positive (60, 61), for those sweet innocents who still retain faith in meta-analysis.

It is possible that a significant contributing factor to my observation of the poor effect of Li augmentation, was the fact that I was using clomipramine in a great majority of patients, rather than one of the other tricyclics. It may be that lithium is working partly, or mainly, through a ‘serotonergic’ mechanism. In that case adding it to a non-serotonergic TCA like amitriptyline might produce a more marked improvement than adding it to clomipramine or tranylcypromine. It is interesting that the evidence for augmenting of SSRIs is also modest (62).

‘Atypical’ antipsychotics

Evidence in the last ten or so years has led many to favour augmentation strategies with so-called ‘atypical’ antipsychotics, which are now in most guidelines for partial and non-responders, at the same stage of the algorithm as switching or combination strategies (63, 64).

Atypical antipsychotics* are even more heterogeneous, as a group, than TCAs, so it is meaningless to lump them together. I have no 1st-hand experience of the degree of benefit from any of them but note there are good reasons for using them with caution** and for regarding this to be a questionable idea for several reasons, not the least of which is that they (‘neuroleptics’) generally reduce DA, when we think increasing DA is what is needed in depression.*** Perhaps low doses of weak DA antagonists, like quetiapine, increase DA through preferential blockade of pre-synaptic receptors? Sadly, there is a paucity of data and a great deal of uncertainty about both mechanism and benefit. Then there is the seriously under-recognised problem that once they have been started they tend not to be ceased, even when a lack of benefit is clear. Patients may thereby be exposed to major side effect problems for no reason. That is a serious error.

Questions like: which one? what dose? and for how long? need to be answered, and promptly, because of these significant SEs, especially in the medium to longer term, not to mention the enormous expense.

*most so-called ‘atypical antipsychotics’ (e.g. risperidone) have tenuous claim to that ill-defined epithet.

**A suggestion of a causal link to increased mortality in older patients (65-72).

***For discussion about this point see here

I do not want to seem too much like a picky scientific purist here, but it is very important to understand that the drugs in question, these so-called atypical antipsychotics, cannot be meaningfully grouped together and most of them are probably not significantly different to the older neuroleptics like CPZ and thioridazine, despite the vociferously made claims for them. For instance, risperidone is a pure D2 antagonists, and a very potent one at that, which is related to the old classic antipsychotic haloperidol. On the other hand, quetiapine is closely related to the prototypical tricyclic sedative promazine, and like promazine is a potent antihistamine with very weak D2 antagonism. I could go on ….

and, needless-to-say, good old chlorpromazine (73, 74). And, one could say vis a vis promazine> chlorpromazine> quetiapine, ‘the wheel is come full circle’ (Edmund, King Lear):

Quite how it is possible to produce a convincing, or even a vaguely plausible, theoretical explanation of how all these different drugs could possibly be effective augmenting agents (almost) escapes me (see ‘pre-synaptic inhibition’ above). Bayesian reasoning indicates that the weak evidence of efficacy is very likely to be wrong (30, 75, 76).

Is anyone else getting a sense of Déjà Vu? Remember thioridazine in depression? e.g. See review of Robertson & Trimble (77).

The theory is weak, the evidence is weak, but the money is good, hugely good. Even Zuckerberg’s eyes would water. $$$, tens of billions, indeed, probably hundreds of billions by now. I do not think we need Einstein’s help on this one.

In summary: ‘A triumph of hope over experience’. Johnson: Boswell's Life of Samuel Johnson, 1791

Further considerations about algorithms

The issue of ‘knowing the pharmacology’ is where some of the experts producing these algorithms have a little catching-up to do. There are some unfortunate errors.

Prominent among the various ‘guidelines’ or ‘algorithms’ are: American Psychiatric Association, British Association for Psychopharmacology (78), Canadian Network for Mood and Anxiety Treatments (79), National Institute for Health and Clinical Excellence (80, 81), Texas Medication Algorithm Project (82), and World Federation of Societies of Biological Psychiatry (83).

One example of a serious mistake is the suggestion that it is OK to combine imipramine with MAOIs, and moclobemide with SSRIs (84) — that has a risk of inducing fatal serotonin toxicity. Yet others lump all the TCAs together as if they are inter-changeable; then there is recommending the worst of all the SSRIs, fluvoxamine, as a 1st line drug: if fluvoxamine (or, indeed, fluoxetine) was submitted for registration by the FDA now it would be unlikely to be approved, and for good reason (46). Such criticisms can hardly be over-emphasised, since this concerns expert panels of specialists. Nevertheless, I can assure readers that if some of the above advice was followed and it led, as it could do, so easily, to serious adverse outcomes for patients, then the doctor involved would find themselves on the losing side in an expensive malpractice suit.

Lastly, recommending moclobemide as a 1st or 2nd line AD — IMCO, is absurd; moclobemide has no useful AD effect. For a drug that has the most benign side-effect profile ever (one would therefore expect it to be very popular), its use has shrunk, almost into oblivion. This is a prime example of a mis-placed faith in meta-analysis.

The ‘CANMAT’ document (79); ‘3.6. How Do second-generation antidepressants compare in efficacy?’ among other things, reports ‘mirtazapine*** is more effective than SSRIs and venlafaxine’ — we are back in ‘Penrose stairs’ territory here. As with moclobemide, anyone who finds mirtazapine to be an effective AD is not treating biological depression and research that claims it is more effective than SSRIs and venlafaxine is, IMCO, not credible. This is an example of where ones’ clinical experience and judgement must over-ride the artefacts generated by pseudo-scientific trials and the statistical legerdemain of meta-analysis — see these refs on this key issue (3, 5).

***Calling mirtazapine a ‘2ndG’ drug is a bit of a stretch — it was ‘invented’ 50 years ago! ‘2ndG’ is a marketing term and has no basis in pharmacology.

Committees do produce some strange ideas and conclusions, and intellectual excellence less-frequently prevails in the results.

There has long been great confusion among psychiatrists concerning what is, and is not, safe to combine. Speaking with my pharmacologist’s hat on, I regard with despondency some of the suggestions and recommendations that have been made, and with dismay at the poor standard of knowledge exhibited in supposedly informed academic work (glaring examples of this are scattered throughout my commentaries).

From the many possible examples of poor knowledge I will cite, from the recent literature, something that coincides with my experience (e.g. (53)) of the commonly expressed errors and misconceptions (57, 85, 86). The commonest combination used, as indicated by clinical practice surveys, has been a combination of fluoxetine with amitriptyline (in-a-word — very stupid, because of multiple CYP450 interactions of potentially toxic degree). The contents of this commentary, and the information on website, highlight the fact that this is one of the more ill-advised combinations that you could dream up. It is axiomatic that any doctor prescribing a drug must have a sound basic knowledge of its mechanism of action and ill-effects. Such a level of knowledge would make it plain that this was a high-risk combination, and if significant ill effects eventuated, the doctor would have a poor defence in law in the case of a negligence action brought against them.

Many of the suggestions and recommendations that are widely made and accepted are simply ill-advised or plain wrong (see my other commentaries and my published papers that address these issues in detail). As an expert in this field I have practised and published concerning various drug combinations over many years. Indeed, that is why I am an internationally recognised expert in serotonin toxicity, that is the ultimate drug interaction with antidepressant drugs that psychiatrists still misunderstand, decades after they could/should have mastered it (as exemplified by the incorrect information in some current guidelines). It is about the only way that psychiatrists can kill somebody within 12 hours by giving the wrong drugs e.g. Otte et al. (87). And yet, still, they try. I have frequent enquiries via the web site concerning ill-advised, and even dangerous, combinations that specialists have contemplated, or tried to initiate: fortunately, some patients have educated themselves better than their doctors (especially those who have learned from this website and my papers).

Other combinations

Here are one or two other ‘adventurous’ combinations that have no obvious (major) problems and at least some plausible theoretical merit — a little caution re 2D6 interactions may be required, e.g. see here:

Venlafaxine + bupropion (sig. 2D6 inhibitor, and both drugs are slightly seizure promoting).

MAOI + Mirtazapine (instead of NTP) + bupropion

Selegiline* + bupropion + NTP

Moclobemide** + nortriptyline + bupropion (but not Moc + SSRI)

* Selegiline (trans-D patch), difficult to put in a cost-conscious algorithm. It is a weak AD and very expensive. Does not boost DA as much as TCP. For many people, does not justify the time-delay and expense as a pre-TCP treatment: again, ‘critical-path’ considerations aid with such decisions.

**I'm not a fan of this drug, it is clearly a drug with such weak effects that it is of no practical therapeutic use as monotherapy. However, it clearly does do something because it aggravates the toxicity of overdoses of SSRIs (see section on ST). There is therefore some small theoretical merit in the notion that it might be worth using it in combination, but not with SRIs, because even in therapeutic doses it can lead to toxicity.

Concluding remarks

Guidelines and algorithms have good features, especially, as Bauer et al. highlight, in reducing unnecessary ‘scattershot therapies’ and the untimely (too soon and too late) switching of treatment measures (88). No guide suggests using three, four, or even five SSRIs sequentially: yet if I had a dollar for every poor patient I have heard of, who has been subjected to that pointless parade of pills, I would be wealthy.

Not all algorithms give sufficient weight, as Bschor et al. recommend (89) to systematic evaluation of the clinical severity after each treatment trial, especially the assessment of deleterious effects of functional changes: that is where the ‘critical path’ precepts kick in and indicate faster progression to the next step.

There is generally an under-emphasis on the treatment of residual symptoms (90), and that is the ‘other-side-of-the-coin’ of the fact that doctors often settle for incomplete response in clinical practice: i.e. they fail to set and pursue the goal of achieving complete remission.

Comments and pronouncements in relation to combining or swapping from one drug to another are frequently pharmacologically incorrect and lead to delays in the progression through the treatment algorithm.

MAOIs are much under-used: I have written a deal about that over the years: my recent editorial (2017), review and diet monograph, sum up key information and references (52-54).

It is hard to avoid the conclusion that the training of psychiatrists in therapeutic psychopharmacology still leaves something to be desired. A report-card might read ‘poor standard, consistently near the bottom of the class, must try harder’.


Note 1

‘For how long has the drug been administered?’ Anyone who cannot answer that is unlikely to be able to make a logical decision about the optimal next step.

My first entry in my medical notes, after the date of the appointment, would always be ‘T2/12, D3/52; the shorthand for the total time on drug and the time on the present dose (2/12 = two months, 3/52 + three weeks). If you have not got that information in the forefront of your mind every time you see a patient, you are not going to make logical treatment decisions in a timely manner.

The key assessment is whether there is a definite improvement in energy and motivation on the one hand, and ability to get pleasure, enjoyment and satisfaction on the other. That is complemented by a critical assessment of the level of functioning in work, social and leisure activities.

Note 2

There is a too easy and uncritical acceptance of partial improvement and of continuing in that state with unchanging treatment.

Doctors’ weak and acquiescent acceptance of partial improvement sets the scene for their all-to-frequent avoidant behaviour. By this I mean giving doctors an easy excuse not to progress to treatments that they are less competent and confident to manage. These, they persuade themselves, are higher risk and ‘after all there is a useful degree of improvement, so perhaps I should just settle for that’, ‘do no harm’ they seem to say to themselves. This is doctors treating themselves, not the patient.

Such thoughts and attitudes are a distortion of the mis-understood aphorism of ‘Primum non nocere’ (first, do no harm). Contrary to popular opinion, that has nothing to do with the ‘Hippocratic oath’ and has only a tenuous claim to validity as a moral precept (91).

I make no apology for proffering the view that a proportion of doctors simply do not have ‘therapeutic balls’. In a publication, a little while ago, I described the attitudes to the use of MAOIs exhibited by many doctors as ‘pusillanimous’ (92), and I have never resiled from that view (54).

Note 3: trial methodologies

A comment about the methodologies used in trials, like STAR*D, it is relevant. It is inevitable and undeniable that any patient sample generated for such trials will contain individuals who were incorrectly diagnosed and/or are never going to respond to medication. Therefore, sequential steps will contain a larger and larger proportion of such patients, compared to sample in previous steps. That will make the treatments used in the subsequent steps seem, artefactually, to be effective in a smaller % of the sample.

Next, a useful methodology for demonstrating the effectiveness of a treatment is a discontinuation trial. Indeed, one of the most powerful methodologies is to use a sample of patients who have already responded decisively to antidepressant treatment, especially ECT.Indeed, ECT is already a natural example of this trial model, which is made even more clear by the relatively discreet and decisive response of typical patients to ECT. That generates a good sample of ‘biological’ depressive illnesses. Relapse is quite frequent, soon after ceasing a course of ECT (~ 50%), for a recent review see (93). The ability of different drug treatments to prevent that relapse is an instructive index of their effectiveness. It is possible to look at the data that has been generated by previous trials: that shows nortriptyline may be more effective at preventing relapse than venlafaxine (and citalopram) and also has less SEs and better benefit on cognitive function (94-96). In this context, we must also remember lithium’s proven benefits: Prudic et al. (97) ‘monotherapy with NT was distinctly inferior in relapse prevention compared to combination NT-Li’.; for recent review see review Rasmussen (98).

A cross-over trial is like a discontinuation trial and, as explained above, if you swap from amitriptyline to clomipramine quite a lot of people get a better improvement. If you do the opposite, most of them get worse.

Many supposed differences or advantages to do with AD drugs are small or of uncertain meaning or usefulness, but when various signposts all point in the same direction it may pay to heed them: also, Bayesian logic re-enforces the power of some conclusions, especially, that CMI is more effective than other TCAs, and that the newer SNRIs (venlafaxine etc.) may be less effective than CMI (and also than the NTP + Sert combination).

I strongly advocate an approach which weighs evidence using Bayesian reasoning. I will give an example of that. There is quite a lot of evidence that drugs that boost either serotonin, or noradrenaline, have a useful effect as antidepressants. Various trials done with the older TCAs suggested the superiority of clomipramine over other TCAs (99-101). Clomipramine is the only one of the TCAs which significantly boosts both serotonin and noradrenaline (38). If you swap people from amitriptyline to clomipramine quite a lot of them get a better improvement. If you do the opposite, most of them get worse. It adds up, one does not have to be a rocket scientist when most of the signposts are pointing in the same direction.

On the other hand, consider a drug like trazodone which does not boost either serotonin or noradrenaline. Although, as with moclobemide, it is possible to find ‘meta-analyses’ suggesting it is an effective AD — it is in the guidelines (102, 103), but I do not know any psycho-pharmacologists who think it is good for serious depression. Bayesian reasoning tells us that we would want much stronger evidence that trazodone, or moclobemide, or vitamin A, was effective, because it simply does not do what we think needs to be done to improve depression. Logic dictates that we assign a lower level of confidence to the possibility it is an AD.

Thank you Thomas Bayes.

Note 4

I remember how often patients coming for a second opinion, or relating the results of some previous treatment, would say how much better they felt. After a few questions, it would be perfectly clear that they were not in fact functioning any better: e.g. they had not got back to work, their social and relationship functioning was no different than previously, and they had not increased participation in the usual hobbies and pastimes and pleasures of their previous life-style.

In response to the reply of ‘how much better they felt’ I would sometimes say ‘It doesn’t matter how you feel’. After allowing time to register being surprised by that comment, from a supposedly caring psychiatrist, I would go on to explain that this subjective impression of how they felt (frequently loaded by situationally driven subjective symptoms such as anxiety) was less important than whether they had tangible improvement in the drive, motivation and energy to do activities that they had ceased doing — but had previously done whilst well — and also getting a proper degree of fun, pleasure, satisfaction, enjoyment, fulfilment etc. from reading, listening to music, socialising, hobbies and pastimes, all the usual culprits (i.e. anergia and anhedonia).


1.               Connolly, KR and Thase, ME, If at first you don't succeed: a review of the evidence for antidepressant augmentation, combination and switching strategies. Drugs, 2011. 71(1): p. 43-64.

2.               Wijkstra, J, Schubart, CD, and Nolen, WA, Treatment of unipolar psychotic depression: the use of evidence in practice guidelines. World J Biol Psychiatry, 2009. 10(4 Pt 2): p. 409-15.

3.               Goodman, NW, Who will challenge evidence-based medicine? J. R. Coll. Physicians Lond., 1999. 33(3): p. 249-51.

4.               Sniderman, AD, LaChapelle, KJ, Rachon, NA, and Furberg, CD, The necessity for clinical reasoning in the era of evidence-based medicine. Mayo Clin. Proc., 2013. 88(10): p. 1108-14.

5.               Sackett, DL, Rosenberg, WM, Gray, JA, Haynes, RB, et al., Evidence based medicine: what it is and what it isn't. BMJ, 1996. 312(7023): p. 71-2.

6.               Parker, G, Is depression overdiagnosed? Yes. BMJ, 2007. 335(7615): p. 328.

txt at:

7.               Parker, G, Roy, K, Hadzi-Pavlovic, D, Mitchell, P, et al., Subtyping depression by clinical features: the Australasian database. Acta Psychiatr. Scand., 2000. 101(1): p. 21-8.

8.               Parker, G, Defining melancholia: the primacy of psychomotor disturbance. Acta Psychiatr Scand Suppl, 2007(433): p. 21-30.

9.               Parker, G, McCraw, S, Blanch, B, Hadzi-Pavlovic, D, et al., Discriminating melancholic and non-melancholic depression by prototypic clinical features. J Affect Disord, 2013. 144(3): p. 199-207.

10.             Hedlund, JL and Vieweg, BW, The Hamilton rating scale for depression: a comprehensive review. Journal of Operational Psychiatry, 1979. 10(2): p. 149-165.

11.             Williams, JB, Standardizing the Hamilton Depression Rating Scale: past, present, and future. Eur. Arch. Psychiatry Clin. Neurosci., 2001. 251 Suppl 2: p. II6-12.

12.             Bagby, RM, Ryder, AG, Schuller, DR, and Marshall, MB, The Hamilton Depression Rating Scale: has the gold standard become a lead weight? Am J Psychiatry, 2004. 161(12): p. 2163-77.

13.             Ostergaard, SD, Bech, P, Trivedi, MH, Wisniewski, SR, et al., Brief, unidimensional melancholia rating scales are highly sensitive to the effect of citalopram and may have biological validity: implications for the research domain criteria (RDoC). J Affect Disord, 2014. 163: p. 18-24.

14.             Fried, EI and Nesse, RM, Depression sum-scores don't add up: why analyzing specific depression symptoms is essential. BMC Med, 2015. 13: p. 72.

15.             Hamilton, M, Rating depressive patients. J Clin Psychiatry, 1980. 41(12 Pt 2): p. 21-4.

16.             Hamilton, M, Frequency of symptoms in melancholia (depressive illness). Br J Psychiatry, 1989. 154: p. 201-6.

17.             Snaith, RP, Hamilton, M, Morley, S, Humayan, A, et al., A scale for the assessment of hedonic tone the Snaith-Hamilton Pleasure Scale. Br J Psychiatry, 1995. 167(1): p. 99-103.

18.             Parker, G, Fink, M, Shorter, E, Taylor, MA, et al., Issues for DSM-5: whither melancholia? The case for its classification as a distinct mood disorder. Am J Psychiatry, 2010. 167(7): p. 745-7.

19.             Parker, G, Fletcher, K, Barrett, M, Synnott, H, et al., Inching toward Bethlehem: mapping melancholia. J Affect Disord, 2010. 123(1-3): p. 291-8.

20.             Carroll, BJ, Bringing back melancholia. Bipolar Disord, 2012. 14(1): p. 1-5.

21.             Parker, G, McCraw, S, Hadzi-Pavlovic, D, Hong, M, et al., Bipolar depression: Prototypically melancholic in its clinical features. J Affect Disord, 2012.

22.             Carroll, BJ, Biomarkers in DSM-5: Lost in translation. Aust NZ J Psychiatry, 2013. 47(7): p. 676-8.

23.             Heres, S, Davis, J, Maino, K, Jetzinger, E, et al., Why olanzapine beats risperidone, risperidone beats quetiapine, and quetiapine beats olanzapine: an exploratory analysis of head-to-head comparison studies of second-generation antipsychotics. Am J Psychiatry, 2006. 163(2): p. 185-94.

24.             Parker, G, Anderson, IM, and Haddad, P, Clinical trials of antidepressant medications are producing meaningless results. Br J Psychiatry, 2003. 183: p. 102-4.

25.             Pereira, TV and Ioannidis, JP, Statistically significant meta-analyses of clinical trials have modest credibility and inflated effects. J. Clin. Epidemiol., 2011. 64(10): p. 1060-9.

26.             Tendal, B, Nuesch, E, Higgins, JP, Juni, P, et al., Multiplicity of data in trial reports and the reliability of meta-analyses: empirical study. BMJ, 2011. 343: p. d4829.

27.             Ioannidis, JP and Karassa, FB, The need to consider the wider agenda in systematic reviews and meta-analyses: breadth, timing, and depth of the evidence. BMJ, 2010. 341: p. c4875.

28.             Fava, GA, Clinical judgment in psychiatry. Requiem or reveille? Nord J Psychiatry, 2013. 67(1): p. 1-10.

29.             Fountoulakis, KN, Samara, MT, and Siamouli, M, Burning issues in the meta-analysis of pharmaceutical trials for depression. J Psychopharmacol, 2014. 28(2): p. 106-17.

30.             Naci, H and Ioannidis, JP, How good is “evidence” from clinical studies of drug effects and why might such evidence fail in the prediction of the clinical utility of drugs? Annu. Rev. Pharmacol. Toxicol., 2015. 55: p. 169-189.

31.             Dechartres, A, Altman, DG, Trinquart, L, Boutron, I, et al., Association between analytic strategy and estimates of treatment outcomes in meta-analyses. JAMA, 2014. 312(6): p. 623-630.

32.             Button, KS, Ioannidis, JP, Mokrysz, C, Nosek, BA, et al., Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci, 2013. 14(5): p. 365-76.

33.             Kumar, A and Chakraborty, BS, Application of critical path analysis in clinical trials. J Adv Pharm Technol Res, 2016. 7(1): p. 17-21.

34.             Cipriani, A, Furukawa, TA, Geddes, JR, Malvini, L, et al., Does randomized evidence support sertraline as first-line antidepressant for adults with acute major depression? A systematic review and meta-analysis. J Clin Psychiatry, 2008.

35.             Carlat, D, Evidence-based somatic treatment of depression in adults. Psychiatr. Clin. North Am., 2012. 35(1): p. 131-42.

36.             Cooper, JM, Duffull, SB, Saiao, AS, and Isbister, GK, The pharmacokinetics of sertraline in overdose and the effect of activated charcoal. Br J Clin Pharmacol, 2015. 79(2): p. 307-15.

37.             Hicks, JK, Bishop, JR, Sangkuhl, K, Muller, DJ, et al., Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2D6 and CYP2C19 Genotypes and Dosing of Selective Serotonin Reuptake Inhibitors. Clin Pharmacol Ther, 2015. 98(2): p. 127-34.

38.             Gillman, PK, Tricyclic antidepressant pharmacology and therapeutic drug interactions updated. Br J Pharmacol, 2007. 151(6): p. 737-48.

39.             Hiemke, C, Baumann, P, Bergemann, N, Conca, A, et al., AGNP consensus guidelines for therapeutic drug monitoring in psychiatry: update 2011. Pharmacopsychiatry, 2011. 44(6): p. 195-235.

40.             Ingelman-Sundberg, M and Rodriguez-Antona, C, Pharmacogenetics of drug-metabolizing enzymes: implications for a safer and more effective drug therapy. Philos. Trans. R. Soc. Lond. B. Biol. Sci., 2005. 360(1460): p. 1563-70.

41.             Gillman, K, Venlafaxine-lithium toxicity: suitability for use in the elderly. J. Clin. Pharm. Ther., 2007. 32(5): p. 529-31.

42.             Patel, K, Allen, S, Haque, MN, Angelescu, I, et al., Bupropion: a systematic review and meta-analysis of effectiveness as an antidepressant. Ther Adv Psychopharmacol, 2016. 6(2): p. 99-144.

43.             Li, DJ, Tseng, PT, Chen, YW, Wu, CK, et al., Significant Treatment Effect of Bupropion in Patients With Bipolar Disorder but Similar Phase-Shifting Rate as Other Antidepressants: A Meta-Analysis Following the PRISMA Guidelines. Medicine (Baltimore). 2016. 95(13): p. e3165.

44.             Galling, B, Calsina Ferrer, A, Abi Zeid Daou, M, Sangroula, D, et al., Safety and tolerability of antidepressant co-treatment in acute major depressive disorder: results from a systematic review and exploratory meta-analysis. Expert Opin Drug Saf, 2015. 14(10): p. 1587-608.

45.             Gillman, PK, Monoamine oxidase inhibitors, opioid analgesics and serotonin toxicity. Br. J. Anaesth., 2005. 95(4): p. 434-441.

46.             Gillman, PK, Drug interactions and fluoxetine: a commentary from a clinician’s perspective. Ex Op Drug Saf, 2005. 4: p. 965-969.

47.             Gillman, PK, A review of serotonin toxicity data: implications for the mechanisms of antidepressant drug action. Biol Psychiatry, 2006. 59(11): p. 1046-51.

48.             Gillman, PK, A systematic review of the serotonergic effects of mirtazapine: implications for its dual action status. Hum Psychopharmacol, 2006. 21(2): p. 117-25.

49.             Gillman, PK, Triptans, Serotonin Agonists, and Serotonin Syndrome (Serotonin Toxicity): A Review. Headache, 2009. 50(2): p. 264-272.

50.             Gillman, PK, Combining antidepressants: Understanding Drug Interactions is the Sine Qua Non. Adv Psychiatr Treat, 2010. 16: p. 76-78.

51.             Gillman, PK, CNS toxicity involving methylene blue: the exemplar for understanding and predicting drug interactions that precipitate serotonin toxicity. J Psychopharmacol (Oxf), 2011. 25(3): p. 429-3.

52.             Gillman, PK, Advances pertaining to the pharmacology and interactions of irreversible nonselective monoamine oxidase inhibitors. J Clin Psychopharmacol, 2011. 31(1): p. 66-74.

53.             Gillman, PK, “Much ado about nothing”: Monoamine oxidase inhibitors, drug interactions and dietary tyramine. CNS Spectr, 2017: p.

54.             Gillman, PK, Monoamine oxidase inhibitors: A review concerning dietary tyramine and drug interactions. PsychoTropical Commentaries, 2017. 17(1): p. 1-104 Available at: Accessed June 17, 2016.

55.             López-Muñoz, F, Álamo, C, and García-García, P, Combination Strategies in Treatment-Resistant Depression. Melatonin, Neuroprotective Agents and Antidepressant Therapy, 2016: p.

56.             Gillman, PK, Plus ça change, plus c'est la même chose. Pharmabulletin, 1994. 18(1): p. 10-11.

57.             Charpeaud, T, Moliere, F, Bubrovszky, M, Haesebaert, F, et al., Dépression résistante: les stratégies de changement et d’association de médicaments antidépresseurs. La Presse Médicale, 2016. 45(3): p. 329-337.

58.             Anderson, IM, Management of treatment nonresponse, in Handbook of Depression. 2014, Springer: Springer. p. 89-97.

59.             Zhou, X, Ravindran, AV, Qin, B, Del Giovane, C, et al., Comparative efficacy, acceptability, and tolerability of augmentation agents in treatment-resistant depression: systematic review and network meta-analysis. J Clin Psychiatry, 2015. 76(4): p. e487-98.

60.             Nelson, JC, Baumann, P, Delucchi, K, Joffe, R, et al., A systematic review and meta-analysis of lithium augmentation of tricyclic and second generation antidepressants in major depression. J Affect Disord, 2014. 168: p. 269-75.

61.             Bauer, M, Adli, M, Ricken, R, Severus, E, et al., Role of lithium augmentation in the management of major depressive disorder. CNS Drugs, 2014. 28(4): p. 331-42.

62.             Tundo, A, de Filippis, R, and Proietti, L, Pharmacologic approaches to treatment resistant depression: Evidences and personal experience. World J Psychiatry, 2015. 5(3): p. 330-41.

63.             Patkar, AA and Pae, CU, Atypical antipsychotic augmentation strategies in the context of guideline-based care for the treatment of major depressive disorder. CNS Drugs, 2013. 27 Suppl 1: p. S29-37.

64.             Carvalho, AF, Berk, M, Hyphantis, TN, and McIntyre, RS, The integrative management of treatment-resistant depression: a comprehensive review and perspectives. Psychother. Psychosom., 2014. 83(2): p. 70-88.

65.             Elderly dementia patients and neuroleptics: excess mortality. Prescrire Int., 2010. 19(109): p. 210-2.

66.             Bhalerao, S, Seyfried, LS, Kim, HM, Chiang, C, et al., Mortality risk with the use of atypical antipsychotics in later-life bipolar disorder. J. Geriatr. Psychiatry Neurol., 2012. 25(1): p. 29-36.

67.             Lopez, OL, Becker, JT, Chang, YF, Sweet, RA, et al., The long-term effects of conventional and atypical antipsychotics in patients with probable Alzheimer's disease. Am J Psychiatry, 2013. 170(9): p. 1051-8.

68.             Sikirica, S, Marino, M, Gagne, JJ, De Palma, R, et al., Risk of death associated with the use of conventional vs. atypical antipsychotic medications: evaluating the use of the Emilia-Romagna Region database for pharmacoepidemiological studies. J. Clin. Pharm. Ther., 2014. 39(1): p. 38-44.

69.             Gareri, P, De Fazio, P, Manfredi, VG, and De Sarro, G, Use and safety of antipsychotics in behavioral disorders in elderly people with dementia. J Clin Psychopharmacol, 2014. 34(1): p. 109-23.

70.             Piersanti, M, Capannolo, M, Turchetti, M, Serroni, N, et al., Increase in mortality rate in patients with dementia treated with atypical antipsychotics: a cohort study in outpatients in Central Italy. Riv Psichiatr, 2014. 49(1): p. 34-40.

71.             Maust, DT, Kim, HM, Seyfried, LS, Chiang, C, et al., Antipsychotics, other psychotropics, and the risk of death in patients with dementia: number needed to harm. JAMA Psychiatry, 2015. 72(5): p. 438-45.

72.             Mulsant, BH, Blumberger, DM, Ismail, Z, Rabheru, K, et al., A systematic approach to pharmacotherapy for geriatric major depression. Clin. Geriatr. Med., 2014. 30(3): p. 517-34.

73.             Lopez-Munoz, F, Alamo, C, Cuenca, E, Shen, WW, et al., History of the discovery and clinical introduction of chlorpromazine. Ann. Clin. Psychiatry, 2005. 17(3): p. 113-35.

74.             Laborit, H, Huguenard, P, and Alluaume, R, [A new vegetative stabilizer; 4560 R.P..]. Presse Med., 1952. 60(10): p. 206-8.

75.             Flacco, ME, Manzoli, L, Boccia, S, Capasso, L, et al., Head-to-head randomized trials are mostly industry sponsored and almost always favor the industry sponsor. J. Clin. Epidemiol., 2015. 68(7): p. 811-20.

76.             Ioannidis, JP, An epidemic of false claims. Competition and conflicts of interest distort too many medical findings. Sci. Am., 2011. 304(6): p. 16.

77.             Robertson, MM and Trimble, M, Major tranquillisers used as antidepressants: a review. J Affect Disord, 1982. 4(3): p. 173-193.

78.             Cleare, A, Pariante, CM, Young, AH, Anderson, IM, et al., Evidence-based guidelines for treating depressive disorders with antidepressants: A revision of the 2008 British Association for Psychopharmacology guidelines. J Psychopharmacol, 2015. 29(5): p. 459-525.

79.             Kennedy, SH, Lam, RW, McIntyre, RS, Tourjman, SV, et al., Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical Guidelines for the Management of Adults with Major Depressive Disorder: Section 3. Pharmacological Treatments. Can. J. Psychiatry., 2016. 61(9): p. 540-60.

80.             Anon, Depression in adults: recognition and management., 2016.

81.             Goldberg, D, The "NICE Guideline" on the treatment of depression. Epidemiol. Psichiatr. Soc., 2006. 15(1): p. 11-5.

82.             Crismon, ML, Trivedi, M, Pigott, TA, Rush, AJ, et al., The Texas Medication Algorithm Project: report of the Texas Consensus Conference Panel on Medication Treatment of Major Depressive Disorder. J Clin Psychiatry, 1999. 60(3): p. 142-56.

83.             Bauer, M, Whybrow, PC, Angst, J, Versiani, M, et al., World Federation of Societies of Biological Psychiatry (WFSBP) Guidelines for Biological Treatment of Unipolar Depressive Disorders, Part 2: Maintenance treatment of major depressive disorder and treatment of chronic depressive disorders and subthreshold depressions. World J Biol Psychiatry, 2002. 3(2): p. 69-86.

84.             Palaniyappan, L, Insole, L, and Ferrier, N, Combining antidepressants: a review of evidence. Adv Psychiatr Treat, 2009. 15: p. 90-99.

85.             Martín-López, LM, Rojo, JE, Gibert, K, Martín, JC, et al., The strategy of combining antidepressants in the treatment of major depression: clinical experience in Spanish outpatients. Depression research and treatment, 2011. 2011.

86.             Rojo, JE, Ros, S, Aguera, L, de la Gandara, J, et al., Combined antidepressants: clinical experience. Acta Psychiatr Scand Suppl, 2005(428): p. 25-31, 36.

87.             Otte, W, Birkenhager, TK, and van den Broek, WW, Fatal interaction between tranylcypromine and imipramine. European Psychiatry, 2003. 18: p. 264-265.

88.             Bauer, M, Pfennig, A, Linden, M, Smolka, MN, et al., Efficacy of an algorithm-guided treatment compared with treatment as usual: a randomized, controlled study of inpatients with depression. J Clin Psychopharmacol, 2009. 29(4): p. 327-33.

89.             Bschor, T, Bauer, M, and Adli, M, Chronic and treatment resistant depression: diagnosis and stepwise therapy. Dtsch Arztebl Int, 2014. 111(45): p. 766-75; quiz 775.

90.             Nutt, DJ, Highlights of the international consensus statement on major depressive disorder. J Clin Psychiatry, 2011. 72(6): p. e21.

91.             Smith, CM, Origin and uses of primum non nocere--above all, do no harm! J Clin Pharmacol, 2005. 45(4): p. 371-7.

92.             Gillman, PK, More on Mrs Murphy’s beans: or ‘do us a fava’. J Clin Psychopharmacol, 2010. 30(2): p. 215-216.

93.             Jelovac, A, Kolshus, E, and McLoughlin, DM, Relapse following successful electroconvulsive therapy for major depression: a meta-analysis. Neuropsychopharmacology, 2013. 38(12): p. 2467-74.

94.             Martiny, K, Larsen, ER, Licht, RW, Nielsen, CT, et al., Relapse Prevention in Major Depressive Disorder After Successful Acute Electroconvulsive Treatment: a 6-month Double-blind Comparison of Three Fixed Dosages of Escitalopram and a Fixed Dose of Nortriptyline - Lessons from a Failed Randomised Trial of the Danish University Antidepressant Group (DUAG-7). Pharmacopsychiatry, 2015. 48(7): p. 274-8.

95.             Atiku, L, Gorst-Unsworth, C, Khan, BU, Huq, F, et al., Improving relapse prevention after successful electroconvulsive therapy for patients with severe depression: completed audit cycle involving 102 full electroconvulsive therapy courses in West Sussex, United kingdom. J. ECT, 2015. 31(1): p. 34-6.

96.             Sackeim, HA, Dillingham, EM, Prudic, J, Cooper, T, et al., Effect of concomitant pharmacotherapy on electroconvulsive therapy outcomes: short-term efficacy and adverse effects. Arch. Gen. Psychiatry, 2009. 66(7): p. 729-37.

97.             Prudic, J, Haskett, RF, McCall, WV, Isenberg, K, et al., Pharmacological Strategies in the Prevention of Relapse After Electroconvulsive Therapy. J. ECT, 2013.

98.             Rasmussen, KG, Lithium for Post-Electroconvulsive Therapy Depressive Relapse Prevention: A Consideration of the Evidence. J. ECT, 2014. 31(2): p. 87-90.

99.             Gervasoni, N, Aubry, JM, Gex-Fabry, M, Bertschy, G, et al., Is there a place for tricyclic antidepressants and subsequent augmentation strategies in obtaining remission for patients with treatment resistant depression? Pharmacol. Res., 2009. 59(3): p. 202-6.

100.           DUAG, Citalopram: clinical effect profile in comparison with clomipramine. A controlled multicenter study. Danish University Antidepressant Group. Psychopharmacology, 1986. 90(1): p. 131-8.

101.           DUAG, Moclobemide: a reversible MAO-A-inhibitor showing weaker antidepressant effect than clomipramine in a controlled multicenter study. Danish University Antidepressant Group. J Affect Disord, 1993. 28(2): p. 105-16.

102.           Papakostas, GI and Fava, M, A metaanalysis of clinical trials comparing moclobemide with selective serotonin reuptake inhibitors for the treatment of major depressive disorder. Can. J. Psychiatry., 2006. 51(12): p. 783-90.

103.           Papakostas, GI and Fava, M, A meta-analysis of clinical trials comparing the serotonin (5HT)-2 receptor antagonists trazodone and nefazodone with selective serotonin reuptake inhibitors for the treatment of major depressive disorder. Eur Psychiatry, 2007. 22(7): p. 444-7.